People who are trying to kind of probably at the beginning of their career or maybe you aspiring to be at the beginning of their career and So this is not a this is not a I gave a talk two days ago about creating apprenticeship programs So this is that'd be a talk at people who have that kind of authority and power to be able to create apprenticeship programs There's a hiring mechanism or an onboarding mechanism. This is this is basically career guidance for new programmers. So It doesn't mean it's only like if you're not a beginner like you should leave I'm just but this, you know, obviously just kind of repurpose this for your own like mentoring or coaching that you do or a team leading or whatever it is with with developers So yeah, let me get into it and just talk a little bit about a little bit about my background with this My apprenticeship story, so I was like like like a lot of people in software development kind of late to the game in my mid-20s is when I switched into Technology and learned HTML slowly or slowly switched into technology learned HTML in 1999 and joined a little education startup near Chicago in 2000 and and as a content contributor and HTML editor and all that good stuff and then in order to keep my job as the dot-com bubble burst I Needed to learn pearl And that was like a one of my big first opportunities to become a programmer Started apprenticing quote-unquote into a really good book called software craftsmanship


Welcome to today's episode of the Mindset Mentor podcast. I'm your host Rob Dyle. If you have not yet done so, hit that subscribe button so that you never miss another podcast episode.  And I'll send you an email every single Monday of what I'm focusing on with my mindset to improve and so that you can improve your mindset as well.  Today, I'm going to give you three steps to stop being overwhelmed. And I think this is important because I think a lot of people, especially nowadays, are starting to get extremely overwhelmed with everything that's going on in their life, everything that's going on outside of their life, all of the stuff in the world. And so I was on a group coaching call and one of my clients was talking about something that's going on in their life and they said it's making me overwhelmed and just want to pause. Is there something in your life that's making you overwhelmed? I want you to think about that thing.  Do you have it in the front of your mind? Okay, we're going to talk about it. When I listen to people speak, I listen on two different levels. I listen to what they're saying to me and I try to listen to what is behind what they're saying. I'm trying to listen to the story that their subconscious is actually telling themself about XYZ circumstance. I'm actually way less concerned about what you do. I'm way more concerned about the story that you're telling yourself. And the reason why is because if I can find the story that you're telling yourself because we're all telling yourself a story in some sort of way. If I can identify what the story is and what you are perceiving it to be, it's usually very, I don't want to say easy. It's easier to dissolve whatever the actual problem, quote unquote, problem is to you. So he was saying, blah, blah, blah, and it's making me overwhelmed. And he said, it's making me overwhelmed. So I mean, if you're in the situation, maybe you ever thought like, hey, work is making me overwhelmed. My schedule is making me overwhelmed. My relationship is making you overwhelmed. I'm here to tell you, no, it's not. You might be like, what? No, but Rob, work is so overwhelming. There's so much that I have to do. No, no, no, no, no. It's not overwhelming.  When you say, and there's a small tweak here, but when you say it's making me overwhelmed, you are playing the victim. Work is making me overwhelmed. That's like, oh, there's nothing I can do about it. Work is making me overwhelmed. Oh, my schedule is making me overwhelmed. Oh, my schedule is making me overwhelmed. There's nothing I can do about it. It's playing a victim card. It's not taking a powerful place. It's not stepping up to the play and saying, I'm going to fix this. Listen to these words real quick. It is making me overwhelmed versus I am making myself overwhelmed. It is making me overwhelmed is taking the blame and placing it externally. I am making myself overwhelmed is taking it and making it internal. It puts you in a place of power because it's not actually work that's making you overwhelmed. It's the way that you think about work that's making you overwhelmed. It's not your relationship that's making you overwhelmed. It is what you are thinking about with your relationship that is making you overwhelmed. And so when you can say, I am making myself overwhelmed, it puts you in a place of power. When it is external, you cannot change it. When it is you, you can change it. But it also shifts the way that you think about these things. Because you realize, oh, it's my fault. And when it is your fault, you can change it. Now, a lot of people don't like to make things their fault. They like to make themselves the victim. They like to play the victim card. And the reason why is because it's just easier. It requires no effort to say work is making me overwhelmed. It requires effort in our minds when I go, I'm making myself overwhelmed.  It changes the way that you perceive things when you can say, I am making myself overwhelmed.  The reason why I want everything to be my fault is because I can then change it. It almost, overwhelm almost never comes from what you're actually doing. It doesn't. Overwhelm comes from what you think about what you're doing. Overwhelm is when you have 20 things on your to-do list, right? 40 things. Let's say you have 40 things. Oh my God, it's such a long to-do list. You have 40 things on your to-do list. Why are you overwhelmed? It's not because of the things on your to-do list. It's because you're trying to think and plan out all 40 of them at one time. You're trying to figure out a way to do all 40 of them at the exact same time. And of course, hell yeah, that's overwhelming, but you're the one that's creating the overwhelm. Because you can't do all 40 of them at one time. You can only do one at a time. There's a whole lot less overwhelm when you have a 40 to-do list is 40 items long. And you go, okay, what's number one? Number one is this. Okay, I'm going to take my time and I'm going to work on number one. There's a whole lot less overwhelm when you just work on one thing at a time versus you trying to figure out how to do 40 things at one time. Because there will never be a way to do 40 things at one time, which in itself makes you feel overwhelmed. And so we're going to go through a couple different things. We're going to go through three different steps to help you stop being so overwhelmed and change the way that you look at it, but also start to change the way that you act and things that you do. First tip, number one that I'm going to give you is just do one thing at a time. Don't try to multitask. Don't try to plan everything out. Just figure out the one thing that is the most important thing and do that thing. The thing that I always tell people, I've been saying this for years. If you get overwhelmed with your to-do list and all the stuff that has to be done in one day and you want to be highly productive, not busy, but you want to be productive. And let me explain what busy versus productive is because busy is when you have a to-do list and you're doing all the things that don't really matter. It's when you get to the end of the day and you feel like you got nothing done. You've ever done that before where you're just busy all day long and then at the end of the day you're like, I feel like I got nothing done. Why? Because you were probably doing the things that didn't matter. You were being busy versus being productive. And so instead of having that really hard conversation with one of your employees, then you're working from home, you decided to do the laundry instead. So you decided to go for the easy thing. It's still something to be done, but you decided to go for the easy thing versus the important thing. And so what you do is this. You look at your to-do list, you take out a cue card and on that three by five card, the little index card, you write down the number one most important thing, you write down the number two most important thing, you write down the number three most important thing, and you only focus on those three things today. But one thing at a time, you only focus on number one and you don't do anything else until number one is done. And then if you do happen to get number one done today, even if you spent all day working on number one, I'm fine with that because you're at least moving the needle in your life, which is important. So as long as you get number one done, great. That was a productive day. If you just think about every single day, if you got the most important thing done every single day, how productive you would be, how productive you would feel, where you would be in a year, if you got the most important thing done every single day, you'd be in a great position. So one thing at a time, number one, you get that thing done, then you can move on to number two, then you can move on to number three, and you take that cue card, I keep saying cue card.  And you forget about the other to-do list. You'll still get those things done.  You'll figure out a way to squeak in the laundry and to squeak in all the things that you need to get, pick up the kids and all that stuff. But you want to focus on the things that are the most productive, the things that need to be done. And you do one thing at a time. Why? Because think about this, for instance.  I'm gonna cook you dinner. Right?  It's all perfectly executed because I'm an amazing cook. It's not true, I'm not.  But let's say that it's amazing. It comes out great.  Oh, man, I am so overwhelmed. I'd be like, that's weird because it's just steak and potato and salad.  I'm like, why are you overwhelmed? Because I can't eat all of this at one time. Like, wouldn't that be crazy to try to take an entire plate of food and eat it all at one time?  It'd be crazy, wouldn't it? Why isn't it any different with your to-do list? You eat one bite at a time.  And when you're done with that bite you put the next bite in your mouth.  The entire plate is done. But you only got the plate eaten one bite at a time. So what's the difference between eating that plate and your to-do list? The only difference is the way that you're thinking about it.  That's what you've been doing since you were a child. But the to-do list, we start thinking about all of them at one time. Oh, I have to get all of these things done. No, no, no. One bite at a time. What is the first bite that you have to take out of this to-do list? What's the second bite when you get done with the first bite? Don't focus on the second bite until you know the first bite.  And you do one thing at a time because you are making yourself overwhelmed. Okay, so that's the first thing. One thing at a time. Also, don't try to multitask.  There's also proof that people that multitask put out way lower quality work than people who do what they call single-tasking. So, if you want to be more efficient, you want to be better, put out better work, get stuff done faster and put out better work, you know, that sounds pretty good to me. Faster and better work, single-task. One thing at a time. So that's the first thing. Number two, stop doing too many things. Now, this might sound the same as number one, but it's not. When I say too many things, stop saying yes to everyone. One of the things that I have found that makes people extremely overwhelmed is saying yes to a lot of people's requests. Get better at saying no. And I was reading this morning, I was reading Meditations by Marcus Aurelius. It's incredible if you've never read it. Marcus Aurelius was one of the Roman emperors, and he had a journal that he always kept with him. He was a very, very deep philosopher. And he had a journal. It was actually his diary. And when he died, it was supposed to be burned. That was actually the request from him. But people started reading it and going, holy crap, this is really good, and they turned it into a book. So this poor guy, his diary, it was then turned into a book, but he's dead, so who cares? He doesn't know. And so you can read, it's actually incredible, because you can read the most powerful man in the world's thoughts, because he's journaling to himself and it's his diary, and you can see that he still deals with all of the exact same things that we deal with. All of them. It's literally the craziest thing to see the most powerful man in the world, you know, a couple thousand years ago, 1500 years ago, whatever it was, dealing with the same shit that we deal with today. So when I say do less things, one of the things that he said is if you want more tranquility in your life, do less things. And what he said, the next sentence after that, was don't do less things than the fact of stop doing so much.  What do I mean by depth over distance? You work on becoming better at just a few things. Only do a few things, but do them really, really well. So when I say do less things, I don't mean like take more naps. I mean the amount of things that you do, instead of doing 10 things throughout the day, do two, but do two of them extremely well.  And that's how you start to master something. Find something that you love to do and do it the best that you possibly can every single day. One of the greatest tragedies, I think that has come with the internet, I think the internet's great, it's helping people and it's not a bad thing at all, is I think that it's very easy to learn things, which is awesome. But in turn, a lot of people are becoming a jack of all trades and they're kind of getting good at guitar and then they're kind of getting good at this thing and they're kind of getting good at this thing and then they're kind of getting good at learning about Bitcoin and they're kind of getting good at this and they're spreading themselves so thin, which is what I mean by distance, I'd rather go deeper into very few subjects versus wider and have a vast knowledge of many things. And so stop doing too many things. Start doing things that you love but get really, really deep into what those things are. Now you might be like, well, I work a day job, this doesn't make any sense. Listen to me, whenever you're not working your day job, if you find something that you love, try to master it, try to get better at it and that's what I mean by don't do so many things. Do less things. Then that flows perfectly into number three of the perfect segue, which is stop doing things that you don't love. Stop doing things that you don't love and I was talking to another one of my clients the other day and he was talking through this and he was saying something to me about how he was trying to do something but he kept feeling like mental resistance towards something. I have to do this thing but I feel like mental resistance. I have to do this thing but it's becoming hard for me. It's hard for me, blah, blah, blah. He kept saying that and that alone shows the way he's thinking about it is that it's hard and when it's hard you're not going to go at something as much. Once again he was making himself a victim in this case but he felt resistance and I said let me ask you a question is what you're doing right now even what you want to be doing? And he's like no. And I was like the resistance that you are finding is because you're doing what you don't want to do. Like you're just not doing something that you want to do. One of the greatest strategies I think that people do is that they give up the majority of their waking hours doing something that they don't love doing simply because they get a paycheck doing it. And I think that more people should I know most people want to be out of the job that they have or a job that they love more they might not hate their job, it might be okay but there might be something better for them out there and I think that more people should do more of what they love and if you do what you love you'll get really good at it and if you get really good at it you can charge a lot of money for it you can make a lot of money doing that thing. It takes time, yes, but you're not doing enough of what you love. Not doing enough of what you love to try to master something but you're also not doing enough of what you love as far as like what's fun to you and so one thing that I would love for you to do is this when you get down with this episode today make a list of everything that you love to do. It could be going for a run it could be meditating it could be cold plunges, I don't know there's all kinds of things it could be make a list of everything that you love and then make a list of everything that makes you happy it could be puppies, it could be you know sunrise, it could be whatever it is and many things you possibly can that make you happy so things that you love on one list and things that make you happy on another list and then look at those every single morning and plan out how you can get as many of those things into your day. It's much easier to go and work really hard when your day is also, you know even if you have a job that you're not a huge fan of but outside of that job that you're not a fan of you still have a ton of things that you love to do because a lot of people have a job that they don't love and then they come home and they don't do anything and then they get really sad and depressed because the fact that their entire life is made up of this job that they hate to make some tired well instead of that what if you were to try to figure out okay I've got this thing that pays the bills let me figure out how to put as many of these things that I love to do and as things that make me happy into my day as many of them as possible that alone will make you feel better and it'll make you feel less overwhelmed and so those three things once again is do one thing at a time number two stop doing too many things try to get really good at a couple things and then just do more things that you love doing if you do that it will be much more natural for you to not be overwhelmed and then the second thing on top of that is what I said first which is change the way that you're looking at overwhelm you are not being overwhelmed by work you are overwhelmed by the way that you're looking at work instead of saying it is making me overwhelmed say I am making myself overwhelmed that alone will flip your perception of the way that you're looking at it it will put you into a place of power which when you're in a place of power you can actually change it so that's what I got for you for today's episode if you love this episode please share it on your Instagram stories and tag me at robdialjr I love seeing you guys in the photos that you take and where you're listening to it on a run or on a walk or with your children all of those things so if you do that I would greatly greatly appreciate it I don't respond to every single one of them but I see every single one of them but I literally look at every single one of them because I love to see where you guys are at and what you think about and all of that stuff as well so once again it is Rob Dial Jr. R-O-B-D-I-A-L-J-R and I'm going to leave the same way I leave you every single episode make sure your mission makes someone else's day better I appreciate you and I hope that you have an amazing day


Thank you so much for joining today. I, you know, when I think about an architect who is not just expanding practice, because it's such an easy term these days, but really kind of very precisely connecting a certain number of dots in particular, I think, architecture, art, and performance, and you've been kind of redefining that relationship through your work. I think about your book, Dark Space, which was really so interesting, but also most recently your performance lecture, which we invented the format of the lecture at GISA, called Working on Water and kind of dematerializing architecture through performance. And so wanted to hear a little bit more about that focus that you're bringing to your work and what kind of doors you feel you're opening through these lenses coming together and how you've brought that to your teaching and kind of engaging. It's not so simple to kind of invite students to say, hey, architecture is not just about building. And of course it engages issues, but there are ways to engage those issues through means that are different. And so I wanted you to expand a little bit on that nexus that you're kind of pushing and I think pioneering in new ways. Yeah, well, sure. And thanks for having me, Amal. I mean, I think that, you know, with the book Dark Space, what I was interested in, and I think this is maybe kind of overall our issues of representation, but particularly with that book was bringing in some of the stories into the architectural canon, which had been excluded or left out. So for example, stories about Paul Revere Williams, who was one of the first registered African American architects and who had a very extensive practice in Southern California, which ran for a number of years and was posthumously awarded the AIA Gold Medal just a few years ago, but virtually had not been written about in the history books, or included in books about housing. And he designed several housing projects. He was actually part of the LA modernist, mid-century modernist scene with Schindler and those guys. So that book was about bringing other things to the canon. More recently, I think what I am interested in is, I won't call it decolonizing the canon, but doing perhaps what Edward Said calls a kind of contrapuntal reading of the canon. And for Said, I think it was in culture and imperialism. He writes about the great English novel and when one sort of digs into it, one or you see references to India and Jane Eyre, that was possible because of British imperialism, which extracted resources, extracted labor, which then made a certain kind of lifestyle possible for the characters like Rochester or what have you in Jane Eyre. So I'm interested in terms of architecture and my most recent work in doing a contrapuntal reading of architecture. So when we read about Lord Corbusier's affair with Josephine Baker, and then also read about Corbusier visiting brothels in Turkey or in Algeria and then making sketches of women nude sketches. And then writing later that he learned about the plasticity of structure from those drawings and we look at his anthropomorphic shapes in Villa Savoie or Villa Stein de Manzi, you know, historically we were taught, oh, this is in reference to his purest paintings, but actually it goes much, much deeper than that. And so it was not just an aestheticization in terms of Baker's body, but it was always the kind of presence of an other thing there that had perhaps not been exposed. And I think that's what's going on in art, in contemporary art these days. And so bringing performance as a, as a other means of representation into my work in order to expose, let's say the dark presence that's always been present in modern architecture is kind of what I'm interested in right now. And it's interesting, because I've heard you mention, and I think we all feel at times that architecture comes a little late to the game, or that certain explorations happen in literature and art before they are really brought into architecture.  And in that sense, I was curious, you know, one of the aspects that was so powerful in the performance of working on water is the collaboration. I think you've mentioned that more and more you're collaborating, not just, you know, we use collaboration again very easily and, you know, as architects, but you're really collaborating with artists, with visual artists, and to think about what collaboration means for you in terms of your practice today, hybridizing or opening up architecture through engaging other creative practices. Well, I think that, you know, for me, it actually goes back to my education at Columbia. I was a student at what we called it Columbia, not yet GSAP in the late 80s, early 90s. And within the curriculum, within the readings, I remember in first year having to read Roland Barthes and Cameron Lucida and the work of art and the age of mechanical reproduction. So, so curriculum wise, there was always sort of, let's say, discourses besides architecture, if you will, in terms of where the brackets were, the brackets were open to bring in literature, to bring in, to bring in art, and, and then early on in my practice in terms of some of the mentors that I've had, you know, I worked for Stephen Hall for a couple of years, and Stephen would always tell us, every Saturday, go out and see some art, so he would close the office for two or three hours to go out and see art and galleries. And so that interest in art has kind of always been there. And I think, you know, now is really becoming more prevalent in terms of meeting artists, working with artists, artists wanting also to collaborate with architects. So it's not just been a one way, but a kind of two way street. And artists actually being, at least the artists that I've been working with, really being interested in thinking about space and thinking about architecture in terms of, in terms of their work. So people from Tarkwasi Dyson to Jonathan Gazales to Adam Pendleton, you know, have really made for some fruitful collaborations thus far. And, you know, when you mentioned go see art every Saturday morning, I was thinking, well, this is where New York makes a difference, right? I mean, that where, you know, it's still all there in a way and kind of intersecting. But you've also expanded and, you know, you work with Mabel as part of the Global Africa Lab. And you've looked at Johannesburg and, you know, made those kinds of relational sort of thinking. And I was curious about some of the recent projects of the lab or how you've brought, you know, some of these conversations to a different context, which is maybe similar in many ways and different in others and kind of thing. Yes. When we started Global Africa Lab gal, as we call it, I think I'd first taken a couple of trips to to Southern Africa, maybe a couple of years before we started gal. And those were studio trips, part of our Kenny program, studio trips with students. And actually, I think those studios were actually based around South African artists. And then when we started gal, we were interested in, I guess, a few things, one in which one was the ways in which, at that time, new technologies, digital technologies were being used elsewhere, you know, in the global south and the innovative ways in which they were using mobile phones and technologies, ways that now in the in New York, we're just now swiping our phones to, you know, to get onto the to the subway system, but using phones for those kinds of things have been going on elsewhere in the world for a long time and using data. But also the kinds of conversations which were happening and this was now gal started, I think, in 2012. So almost eight years ago, the kinds of conversations which were going on, which I think were at the forefront in terms of architectural discourse in relationship to critical studies in terms of colonialism, technology, and looking at the African diaspora. And so gal is a sort of platform that has allowed us to do that, not only in terms of looking at situations and during research projects on the continent, but within the diaspora. But one thing that we've sort of made, tried to make certain of it in terms of the continent, is that we're not just kind of looking at sites of crisis. But we're really sort of thinking about gal as investigating conditions of agency. And so whether or not it's the ways in which technology was being used in new ways. And we actually have an ongoing project which is dealing with intellectual mobility, which deals with technology and mobility in the western Cape. But we've also studied with students in Cape Verde, for example, looking at issues of climate and climate change. Cape Verde being a Ten Island archipelago off the west coast of Africa. That was a site for colonialism by the Portuguese. Now is a site that's really being impacted by climate change in terms of not necessarily the abundance of water and sea level rise, but actually the paucity of water and drought. So that network within diaspora has allowed us to kind of look at a variety of things. Now sort of shifting perhaps a little bit more to kind of considering the environment, including the studio that I'm doing this semester, which is a second version of the space of water studio that we did in Cape Verde, but this time based in Cape Town. Mario, just to end, I know you're part of the MoMA upcoming show Reconstructions, Blackness and Architecture in America. And I wondered if you would share a few like a teaser or, you know, something about the show. Very excited to see it. And it's a very long overdue, obviously, and just wanted to hear anything you might want to share with us about it. Yeah, sure. This is the fourth in MoMA's series on, I think it's called Current Issues or Something in Architecture. The previous one or the last one was about housing, I believe. And this one, which is a group show of 10 architects, a little bit more than in the past, who are looking at different American cities and I would say issues of spatial justice, if you will, being architects, issues of spatial justice within these cities, ranging from issues that actually overlap with climate change to civil rights. I happened to be working in Nashville, Tennessee, which is where the Student Nonviolent Coordinating Committee was formed in the 1960s. But I'm really interested in the ways in which, let's say, Black bodies have had to move through space, negotiated space, thereby sort of giving new forms to spacemaking, if you will, as a kind of choreography, sometimes improvisational, sometimes reaction in terms of making space, and what can come out of that or what has come out of that in terms of translating those things into architecture. Really a great pleasure and I'm really both, I know G-SAP, we're so lucky to have you be thinking about those issues and kind of exploring and opening up architecture in ways that it needs to be and it's always inspiring to see your work and then the response of the students to that, to these provocative and crucial questions. So thank you and again, fall is around the corner and this moment we are in certainly while being a time of crisis, I think, is also a time of opportunity to imagine change, right? Yes, yeah. All right, thank you so much Mario. Okay, thanks a lot, I'm all. Okay, bye. Okay, bye-bye.


So, first of all, I would like to thank the organizers for bringing me here. I mean, it has been a wonderful experience, a very nice environment, and I'm actually one of these domain scientists that after a few years has realized of the need to be using different new techniques that have been around for a while. So, if you find something that it's too low level, I mean, this is because machine learning is not the primary knowledge of the group. So, let's see if I can move on. So, since everybody's so young here and everybody is so into new methodologies, I wanted to bring back a very old question, okay? This was posed in the Royal Society before the 20th century, saying, okay, we don't have enough nitrogen. How are we going to feed all the population that we are having if we don't have enough nitrogen to grow our crops? Okay, that was before the starting of the 20th century. And actually, what happened is that it started to raise for different scientists to try to see how we come up with a solution to fix nitrogen from the gas phase into molecules that we could bring to the fields and then grow more crops and eventually, sustainable people. So, yeah, basically you guys are here because there were these two people that were scanning 20,000 different materials, okay? And I want to bring your attention that big data has been here for a while and has been in heterogeneous catalysis for a while, okay? This process is still giving us enough food and it consumes 1.5% of the total energy consumption. No, about 1% of the energy and generates 1.5% of the CO2. Okay, but remember, 20,000 compounds, where does it? Now, we are facing a few challenges at the turning of the new century which is not the turning, it's already the 20th. You know that we are generating all these CO2 on plastics and eventually we will need to refarnish the full chemical industry as we are understanding it. And one of the problems is that we will have different inputs, not the ones that we were having before. Let's see if I can find a point, oh, find a pointer. Oh, I can use that one. Okay, oh, marvelous. Okay, and then that one, okay. Then, so basically we will need to refarnish, yeah, oh, okay. So refarnish all the chemical industry and we will like to take CO2, water, air, electrons and use those to make our new chemicals or basically start using all our dust or our waste and generate new chemicals by different ways. So yeah, so materials for catalysis is one of these sciences that will quest for properties, activity, selectivity, stability and we will ask for, okay, how we prepare these materials. Actually, the final way how we do think is a little bit different than that. We would like to go this way but we end up synthesizing lots of materials, characterizing them and trying to infer the properties from this. And basically this is what is based on our structure activity relationships. Most of the things that you hear in heterogeneous catalysis and electro catalysis and in all this is based on knowing what is the structure of the material in order to be predictive, okay? But it will be nice to go the other way around. Okay, so how does activity look like for a typical catalytic problem? Well, activity looks like these two central panels here, okay? This is a typical volcano where you are having the top of the activity in a region and then you have less activity for the catalytic materials later on. And then you are having also this other problem here that resembles a little bit what Francesca has also in the activity of the materials that if you are looking for selectivity and selectivity is producing just one compound out of the many that you can produce in a chemical reaction, then you are facing a cliff. And these type of problems here are represented here in two dimensions and you see activities, easy selectivity is a pain. For activity, you see that I'm having one term here. This is what is called in our terminology a descriptor, okay? So catalytic people will say this is a descriptor, typically it's a binding energy that provides you that it's the one giving you what is going to be the represent activity of this parameter. So if you are able to tune this parameter, eventually you will get to the top of the volcano, which is what you are aiming. Okay, so these are more as the basis. Now the problem start. Well, let's go one thing. The reason why these volcanoes appear is because there is a lot of symmetry between the properties of different intermediates on surfaces, okay? And by symmetries, I understand that you cannot optimize the different intermediates on the materials, typically because our materials over the years have been very, very simple. Okay, we have been using metals, mostly metals, and when things get too complex, then we were adding oxides and things like that. But actually, there is not a single catalytic formulation in industry that is not more complex than a metal, okay? So these are our academic version of how the things should look like. And as you can see, there is plenty of this, there is plenty of these dependencies here. The transition state, which is controlling the rate of a given process, depends on the energy difference. And you can basically find very many of those that are related, that are related, okay, okay. That are related, that are relating the thermodynamic to thermodynamic properties, some kinetic properties to some thermodynamic or even coordination. So all these rules, what I'm putting is so many symmetries into your system that it doesn't work anymore. So the ultimate consequence of this is what you can see, oi, oi, oi. What you can see here is that on one side, you are having these adsorption correlates on one way, and then the coverage, which is the amount of these particular species that you are having there, correlates the other way. So if you draw the envelope between these two lines, this line here, this line here, this is the origin of your volcano, okay. So now that we know the basics of how people has been addressing these problems, let's see, so traditionally what we were doing was the following. People was testing materials in an experimental lab, we were doing some DFT calculations, basically getting the reaction path. The reaction path is just a list of intermediates and the barriers that are connecting these intermediates, for not only the direct path, but also in principle, also for the non-selective paths that I was explaining before. How about these we were doing, our beautiful volcano was trying to see what was the descriptor of the energies. Now, problem comes because life is much more complex than that, and actually catalytic structure, it's a problem because you have seen defects, modifiers, promoters, subsurface atoms, impurities, you name it. You have a lot of molecular complexity that comes from the fact that you have large molecules, so all these three-dimensional structures are difficult, including chirality, aromaticity, rigidity, we have heard many of those today. And if you go to electrochemical environments, for instance, then you are having even more complexity there. One part comes from the external energy source that you are putting, being these the electric potential or light, but then you can have so many effects, cations, and many other things. So the whole thing is that we can no longer survive in this very easy life of a small bunch of metals and calculating our reactivity there, but actually we need to increase all this complexity. And this comes at the idea of machine learning at different stages to try to understand or try to get information for different things. So the first thing is having a database. And I have heard here, everybody having a database. So Moises Program 1 for us. The good thing of our database here, which contains only computational data, is that it has been trying to generate, I mean, to store data that comes not only from materials, but also from molecules. Our systems are in a continuum phase. And the reason for that is that we have seen that many of the databases were basically based on describing materials or describing molecules as separate issues. And we think that there is a lot of information that can go at the interface between these two things that could be important. So a few characteristics, exactly chemical purpose, project oriented. We have basically a central node so no one can say that it's storing data on us and so on. And actually, I want to say that we managed to commercialize this, which is very surprising from my point of view. But OK, so basically, we have different areas. You can go have your private area, public area. And this is very good for companies that are trying to use it. And while this is a typical pipeline, what is important is we normally do this thing, upload our data for a system, for instance, for a paper that we have submitted. And when the papers accepted, it's when we release the complete full database that it's associated with. Of course, we are using lots of codes, traditional things related to the format and the ontologies that will not work more. And yeah, we have been using the typical chemical markup language in order to make all the translations. OK, that's enough. So it looks like this. It could be a little bit more or whatever. OK, so as I was saying, and one of our most important role when we are trying to do catalysis discovery is to see what is the descriptor for a particular property. OK, and here we started very, very lousy. We started doing principal component analysis. OK, and this is the routine. And at that time, what we were thinking is OK. I mean, we can calculate lots of properties of very simple fragments. And that was at that time, carbon, hydrogen and oxygen and up to C3 fragments containing all of this. And what we tried to see was how many of these principal components we needed in order to in order to represent the energies in the absorption energies in the proper way. So we ended up with these two types of descriptors, the metal descriptors and the molecular descriptors. And of course, because this is principal component analysis, we have to try to map them afterwards in order to see if we could see if we could find any meaning to these to these descriptors that we have found. So this is what we did. And actually, yeah, I mean reproducibility is nice. The test is very nice. But we found two things. The first principal component analysis is something that was known in the literature. So it can correlate to something that was known for very long in the literature. And this was a deep and center. So there was a lot of modeling that was done in the 90s and beginning of the 2000s. That was based on absorption energies only can be I mean, the main descriptor for the absorption energies in metal is just a single parameter. And actually, we managed to retrieve that. And for me, that was the first signal that we could do something useful. Second signal that we could do something that was interesting for us is that we were also able to retrieve what the second principal component was. And this is very much related to the redox capacity. So the capacity to either donate or retrieve density from another object. OK, these are the two properties from the metal. So for me, what is very, very important is that we understand the chemistry that it's coming behind because the chemistry is what I could convey to my synthetic colleagues to go on and try and look for more. OK, of course, we did some testing, how it was working outside this our system. And while I mean, considering the simplicity of the model, it really was very important and very interesting for us. One of the other problems was also mentioned by Riann, complex reaction networks. When it starts to come to investigate, when you start to investigate activity, we have exhausted most of the simple reactions. Ammonia synthesis has been known for many years. We all know all the step by heart. But now when it comes to systems like you start having lots of possible intermediates, the annotation and the propagation of the different and the proper transitions between the different elements becomes important. So we annotated first time for ethylene glycol, all of the composition network. And that was a pain because our aim was trying to see. Now you see that it ends up in a reaction network that it's as complex as this thing here. OK, so you get a reaction profile, but you cannot make any logic out of this reaction profile. And the reason for that is that it needs to be combined with microkinetics so that you can really express what is the major product that you are extracting. OK, so DFT, since we have so much DFT data, we are losing the capability of saying what is the real outcome if we don't couple it to microkinetics. So now situation is even more complex for reactions that are in the electrochemical context. And this is just copper and copper is known to be. D catalyst for electrochemistry, CO2 reduction. OK, now you will now we try to see what people show what people has been doing here. OK, let's see. So in a catalytic in the electro catalytic CO2 reduction, what we would like to have is high products, let's say more than three carbons that are coupled together so that we can form these long chain systems. But actually, the system is a little bit more complex than that. And bugger and colleagues, they realized that they could categorize, they could classify, and this is a classification problem, the properties of the different metals just by separating what is was the absorption energy of CO and what was the absorption energy of hydrogen. Very easy problem. You took all the metals, all the reactivity, and you end up with all these groups. And actually, the only one that it's able to form carbon-carbon bonds is copper. OK, the rest are giving only C1 products and mainly C1 products or hydrogen.  Now, the problem is that if you want to go to C3s and actually you can get C3s, you need to do a few bunch of things. First of all, you need to change the configuration of the cell. This means that the traditional way how catalysts were searched for is not good enough for this particular problem, which means that you need to pass a lot of current. OK, now the second thing is that if you do this, you end up with this very nice list of products. OK, so if you are not a chemist, I can, well, I cannot even draw all the structures here. So even if I'm a chemist, so basically you can generate absolutely everything. But I want to point out a couple of things here. One of them is that you can generate these are the C2 products or the main C2 products, acetylene. It has a carbon-carbon double bond and ethanol has a carbon OH group. It's an alcohol, OK? So basically you see in the C2s, you can generate these problems. But if you go to the C3s, the molecules containing three carbon atoms, then the one that contains the carbon-carbon double bond doesn't exist. Or you cannot generate it in this material for this purpose, OK? So basically in order to figure out how was this problem going on, we had to generate all the structures going from C2, CO2, all the way to C3s and even we have been growing even larger, generate all the combinations, use molecular graphs for this, eliminate the redundancies and structure and level all the individual steps in our reaction networks, OK? It's also possible to do the ball breaking reaction, I mean, detection, so figuring out what will break or what will form. And then you end up with an intermediate kind of code that we generated, which is this one here, but my favorite feature ends up to be that one. So if you go to CO2 reduction up to C3 products, you have a problem because you have 500 intermediates and in principle, 500 intermediates you can handle by DFT, but you have 2,500 transition states linking those. And this is a huge problem because for each calculation that you do for an intermediate, you will need a spring and calculation that it's at least five to 10 times more expensive for the transition states, OK? So basically, this is not doable anymore. So what we did that was to generate all the graph, calculate the intermediates by DFT and then use a series of filters that allowed us to simplify the reaction network down to the point that we wanted it and that's the point that we wanted it. So in here, what you can see is the following is just a piece of the reaction network. And as you can see, this molecule here is the one containing the carbon-carbon double bonds for those that are not chemists in the room. And this is the propanose. So remember, we are getting this, but this one is the expensive one, the one that we would like to have. And actually it comes down to the fact that there is a problem with the kinetics with how accessible these different parts are. And basically we are stuck with an intermediate somewhere here that goes all the way to propanone. And the only way to understand how this reaction works was to go to our colleagues from NUS and EDH and what they were doing was starting by two different related, they're not even intermediates, they are related to the intermediates. They started by two different molecules. And when they started by the molecule that we told them, by the intermediate that we told them, they ended up seeing some propelling here that you can find here in the experiments. So the only way to integrate such a complex reaction network is to put one of the intermediate products and see how it goes. And this is as much experimental information as you can get for these complex reaction networks, which I would say is really a challenge. We have also been working on other types of weird things and this is kinetics trying to generalize performance equations. Let's see. So again, you can see that what we did here was to take, yeah, to take DFT data and then we built a full machinery here that I will try to show better here. So this is the results that were produced in EDH.  OK, so how much is produced of the product that you want. And in the second plot, how much selectivity you are getting to different problems. So this is one of the typical problems of selectivity. Maybe you have good conversion, but then you have selectivity that doesn't work for your project. OK. If you do this in traditional DFT, what you will end up doing is calculating this reaction network, which is very cheap. OK. Now, the problem is the following. Even if we are sure that these materials here are more or less the same size when prepared and this is a crucial problem, what we are not sure is if they transform under two reaction conditions and by transforming, I mean if you are generating some carbon, which is one of your undeserved products, the carbon can go inside the lattice and then you have a carbide. And basically your calculations are wrong because they are calculated on a metal when you are representing a carbide. So when you are having this type of phase changes and this type of modifications of your materials, this kind of approach just based on the DFT profiles doesn't really account for the whole thing. So what we did here was try to see if we could get some parts of the information from the experimental data and then try to see if we could couple this to some descriptors that were obtained by DFT. OK. So this kind of hybrid data, this is what we try to do. And for this, we use an algorithm that was done by my colleagues, Marta Sales and Roger Guimara. And this is basically a Markov chain Monte Carlo that goes and tries to find the best equation for your problem. OK, so this is trying to find 3D equations, I mean, in terms that we can interpret as humans. So basically, let's see if this goes or no. If we go here, they start by a constant and they keep on trying to see, they have learned all the different functionalities for Wikipedia, the most typical formulas for Wikipedia. And basically they keep on adapting the different things. OK. And you can see these evolving over time and it looks very, very nice. Except it has a problem and the problem is dimensionality. So when we put all these intermediates that we have calculated via DFT, we found that there was not possible to reproduce to obtain any selectivity or any conversion pattern. And the reason for that is that the data is so linearly related that in the search for the different materials, it was just scrambling different variables in the main equation. So basically, this was taking us nowhere. But as you have seen, we had a way of sorting this out, which was our small PCA. So what we ended up doing was trying to see what happened if we include our PCA and we could kind of map the different metals and map the different intermediates and use just two absorption energies as the script does for these materials. And then you end up with this equation for the conversion, which is a very, very simple one. OK. Of course, it's only valid in these domains, but basically it contains the full chemistry for these particular systems. And if you keep on running these Bayesian machine scientists for longer, you will get other equations, but many of the motives that you find in the primary structure, they are still fulfilled. OK. So it means it's possible to obtain equations, formal equations for these problems that are based on one or two descriptors exactly in the very same way as we would having for the volcano plots, but these way in a completely automated manner. Of course, you can try to go for selectivity. And I have to say that this is much more complex, but something can be done. Other, the second source of complexity of catalytic systems is just the structure. So I was seeing before, I was telling you before, in the old days, we used to have very simple structures that were metal based. So the complexity here comes, for instance, on these materials. And this is the copper that I was showing you before. If you put copper or copper oxide, because you cannot have copper, basically it oxidizes when you look at it almost and you put it on the potential. You will end up with structures like this one's here. OK. Basically, your system is reorganizing all the time. You have some copper atoms here and then some oxygen leftovers. We don't know how many and we couldn't find how many from these simulations. But at the end, what we were looking at was to see if there were predominant patterns. And this is for the discussion that we were having earlier today. To me, the identification of patterns is one of the bottlenecks in which I will need more support from the machine learning people because we are not really very good at that. And of course, if we know what the patterns are, then we can look for the reactivity of these different patterns and try to see if they are repetitive in the different preparations and try to see how this works. But when you discuss structure, you end up having to have some characterization. And actually, in any catalytic preparation where they spend more money is in characterization, and this is mostly done in academic labs. OK. And I want to show you a proof of concept that we have derived in the group that is very similar to someone was presenting experimental results before. So we have been trying to understand the chemistry of this single atom catalyst. OK. Single atom catalyst is a very easy concept. So you try to use as little as possible of the expensive material. OK. In this case, the expensive is platinum, meridium or so. And many of the scaffolds that we use are based on carbon. So they are really cheap. You can burn whatever and you will have a carbon scaffold. Maybe not the one you want, but that's a different problem. OK.  OK. We would like to use this as sustainable activities for organic synthesis. And the reason for that is that if you are synthesizing something for pharma, you want that your metal stays where the metal should be. And it doesn't go into the pharma because then your pharma cannot pass the FDA or the European levels, of course, because you cannot have that many, many organometallic catalysts, many molecular catalysts have metals. And sometimes they get incorporated into the final product, even in PBMs. And this is really, really bad. So one of these strategies is to try to grab the atom, stay it in the lattice and try to use a proper reactivity, but the stain on the lattice. However, identifying these isolated metals on the scaffold is really, really very difficult. And one of the ways we do it is through high resolution transmission electron microscopy. And then basically the workflow is also follows. You have the image acquisition, which is as complex as it can get. And what we are trying to do here is to automate the image analysis. OK. And then we would like to extract because from these very expensive characterization, we were only getting in the past qualitative information. But if this is automated, we can get much more than we used to get before. So how we do that? OK. So our colleagues in Zurich in the NCCR were able to generate these kind of scaffolds with a lot of metals, which is good. So you can have lots of reactivity. But basically you end up 15 metals and five different types of force. And then you end up with this collection of galaxies. I would say that this is a proper distribution. I mean, the proper name for this. So you can have basically everything. And of course, there is a problem because for each image, an educated person needs 30 minutes to level. OK. This is where the bottleneck of the whole thing is there is a second bottleneck, which is the perception bias. Each different microscopy will level the will level the image in slightly different ways. OK. Slightly different, so not completely different. There is one area here that you can see here, these bright areas, the spectroscopies, they don't believe that they have enough contrast in their eyes. And they will think that they will not level in these particular areas because they are not good at it. OK. So you see lots of human labor, train some biases, intrinsic to the perception. So what we did here was the following. We contacted the group in BSC in Barcelona. And this is why I think Sasso's comment this morning saying, we need to sit down and spend some time together to see what we really need and how we can convey the needs of the different groups. This is really important. It was really important for us here. So there are the experts in the image recognition. What we did was sit with them. They come up with a very nice and cheap solution, which is a convolutional neural net that works really nicely and identifying all the images in about one second. They even spot atoms where the microscopy doesn't. And actually, these are some of the parameters that are related to how robust this methodology is in terms of what is called the position and the recall. And basically, this is a nice way to see if if if the convolutional neural net was working fine. Of course, we only run this for one set of materials that were the platinum based carbon nitrides. And one of the reviewers that was very clever and that was actually our first question was what happens if you change platinum by another metal? Now we took the same exactly the same material, but instead of being prepared with platinum, being prepared with iron and actually the networks and identify it's a single atoms, too. And I think I'm running over time. So I will wrap fast. Basically, this allows us a different thing, too, which is something that the microscopist will complain about doing, which is ranking the positions. I mean, making a 2D map of the positions of all the atoms. So actually, the degree of information that we are getting from these samples in terms of how is the distribution? It's these are random distribution. We can test better models if we are having these things here. And we can also calculate to what extent the different metals can interact. Yeah. And with this, I would just like to conclude. I mean, as you have seen, I mean, machine learning can affect the different levels, the whole field of catalysis. And we need we have challenges in changing the language and how to transfer the different understanding that we are having. And with these, I would like to thank you and thank the group and you for your attention. Thank you. We have time for several questions, maybe just to get it started. You started your talk by talking about these these next databases you had with IOTMBD and then you gave us a lot of stories about how you used your own data sets in some of these predictive models. Do you have any efforts to automatically use things from IOTMBD or things that weren't generated in your group? Have you leveraged other random things that people have put into the database and any of these efforts or have it all been your own students sort of generating these things? So for instance, it's the automatic network recognition and people is subscribing because because the data sets are nice to upload and easy to handle. But still, we don't have people contributing for developing tools. No, which is a pity. Couple of slides ago, you had this convolutional network trying to detect the elements there, right?  But for that, you'd need a lot of labeled data, right? Somebody would have to sit down and draw boxes around it. Did you have to do that? Or did you use some other technique to? No, we had to do that because you still rely on the truth of the microscopes. OK, so you still need the way you train the image manually annotating them. There is no other way so far. But if you come up with one, happy to hear. But you also said that you found some atoms that the microscopist hasn't. Yes. So who's who's right in my case? Yeah, that's that's that's that's that's that's a crucial problem. I mean, that's a crucial problem if we are trying to to use these automated things. There is not a ground truth. And then since there is not a ground truth, there is some room for for discrepancies and I'm upset. I mean, we will have to face this, but it will happen with many techniques that we are that when we are trying to do this smart characterization, we are contrasting them with references. And we think that everything is right. Yeah, yeah. Any other questions? Thank you very much for this amazing talk. So I found this single atom catalysis extremely interesting when you said that you could just replace platinum with iron I think. So can you in principle do this with any metal or are there some limits? So the contrast in this image comes from the square root of of the of the atomic number. OK, so in principle, platinum and iron are not as far as you can get four metals in the periodic table. Now, if you are asking me carbon and nitrogen with a wood, we will need to combine more than one different technique. Microscopy is not good for this. Thanks for the beautiful talk. Have you ever tried any unsupervised learning, like the one Alessandro Lio presented to capture density peaks in, I don't know, color, for example, or would you intend to do so in the future? Not yet. We will try to do that in the future. I mean, this is more characterization project. Both was the easiest thing that we could do. But certainly there are many other techniques that need to be implemented that some of them are very much related to density, so they will come now. OK, let's give her another round of applause. Thanks, Maria.