Hey guys, how you doing? This is your boy, Rich from Rich TV Live and you too can join the club at richpicksdaily.com where you can learn how to win and trade. Guys, how you doing? This is your boy, Rich from Rich TV Live and richpicksdaily.com where you can go and win big. We're talking about cryptocurrencies today, nemesis, doubt, put on your radar, put on your watch list under the crypto trading symbol NMS, nemesis, doubt, a staking token. They also provide you the opportunity to receive rewards staking bonds. So high yields here, very high APY, some of the highest APY I have ever seen exclusively here with your boy, Rich on Rich TV Live. So put the nemesis, doubt on your radar, put on your watch list. We think it has a chance to be a big winner. Let's take a look. This is the nemesis, doubt, finance website. And this is really a project that is focused on people that are interested in OHM time and many OHM fork rebase tokens, otherwise known as DeFi 2.0. And we brought you DeFi first. Now we're talking about DeFi 2.0. This is the speed that we are seeing and the evolution in the cryptoverse. This is the first focused coin that is really on the BSE network. So the BSE audience is the main network users for this coin. And the priority is for people that are interested in crypto, DeFi, DeFi 2.0 staking. This is a coin that really focuses and emphasizes on staking, passive income, DAO. And we're gonna show you guys. I mean, we got so much to go through here today. You can see their list on a hot bit, $203. And what is a DAO? A DAO is a decentralized autonomous organization or DAO is an organization that is governed by code instead of leaders. So this is a DAO project. Anyone that's focused on NFTs, Metaverse or Web 3.0 should be focused on this project, put it on your radar, put on your watch list, NemesisDAO.finance, this is the website, the NemesisDAO, you can connect now a decentralized treasury protocol with reserve currency. The OHM forked extremely successful protocol owned liquidity model on the BSC network launched on the 13th of November, 2021. It's truly revolutionary contract and one of the DeFi's most interesting protocols we have seen lately. We're gonna go through some of this. So this is them on hot bit. You can see the chart, it went up and it's come all the way down but you can see it's trading at $203, NMS, NemesisDAO token. You can see the 24 hour volume on hot bit. You can see the breakdown of what is a DAO. You can see the highest yield on the Binance smart chain. Look at the price on Binance, $202. Look at the APY, 627,182% APY. Just unheard of APY, a market cap of $13,397,986. Total value locked just over $9 million and they currently have a treasury balance. So they actually have money on the balance sheet which is something that I think is phenomenal. I wanna see more projects do this, $11,542,937 in the treasury. One thing I think that's also really good, the NMS supply, only $66,146 and backing per NMS, 174.51, current index, 11.97 times. So you can stake, all you gotta do is hit the stake button and you can stake, you can see here, stake your NMS. All you gotta do is buy some NMS and start staking with enormous returns. You can see here the five-day ROI, 12.72%. So just enormous yields. This is the bond, this is the treasury bond. You can see here the NMS, BUSD, LP. You can see here the NMS, BUSD, LP. Just click add LP or the BUSD. You can see the differences in price and you can see the ROI. You can see how much has been purchased. So there's two different bonds you can purchase and you can see the ROI on each. Obviously this one has a little bit of a higher ROI. This one has a lower ROI, but at a higher price. Click on bond and then you just choose how much you want to stake or how much you want to bond and then click approve. You want to buy NMS, just click on buy NMS, connect your MetaMask. My MetaMask is already connected and then you can start purchasing. This is the DEXtools.io, BUSD, NMS. So you can see here, once again, the chart and you can see the DEX score, 99 out of 100. Project reliability score based on their metrics, they use a very, very high DEXtools score. Once again, this is the bonds and the yields. This is the certex.com, which certifies NMS's DAO audit is in progress currently at 99%. So they are moving along with their audit. This is a truly revolutionary contract and one of the DeFi's most interesting protocols we've seen each NMS token is backed by a basket of assets, BUSD, LPs, et cetera, in the Nemesis Treasury. So when we were talking about the Treasury and we were talking about how we really like the fact that they have money and they have a Treasury, this is how they're doing it. Each NMS token is backed by a basket of assets, the BUSD and the LPs, et cetera, in the Nemesis Treasury, giving it an intrinsic value that it cannot fall below. Nemesis DAO aims to create the go-to reserve currency that the Binance Smart Chain needs while optimizing for short and long-term profit for holders of the NMS Treasury. Their unique project introduces economic and game theatric dynamics into the market through staking and bonding to achieve this. What is the main advantage to the end user? Well, what you can get from this project, Nemesis DAO aims to become the top OHM fork on the Binance Smart Chain and to provide a safety haven for investors where they can park and grow their money and enjoy long-term passive income by auto compounding, which is a game changer. So you want to stake it, you can see here, I showed you guys the NMS, BUSD LP, and the BUSD. Those are the two bonds available, which we talked about, and you can stake the NMS right here with a 12.72% ROI in five days. So some incredible returns in staking and you have the option to also earn rewards through bonds. So that's really what the focus is for this project. Users of Nemesis DAO can buy NMS tokens from decentralized or centralized exchanges. So you can buy on PancakeSwap, there's a lot of different places you can buy them. And this is what it looks like as well if you want to purchase. All right, and this is their telegram. I am joined on their telegram. You can see here some information on the APY on staking, the OHM forked, and once again, the highest APY compared by Market Cap among other OHM forks on the Binance Smart Chain. Strongest liquidity among OHM forks on BSC. And you can see aiming to become the safest OHM fork on the BSC. And then when we go back to Sirtex, that's what they're talking about with safety. So if you're looking for safety, this is a great way to do it, get certified on Sirtex. Secure the World and the Crypto World founded in 2018 by professors of Yale University and Columbia University. Sirtex is a pioneer in blockchain security utilizing best in class AI technology to secure and monitor blockchain protocols and smart contracts. Sirtex mission is to secure the cyber world starting with blockchain. Sirtex applies cutting edge innovations from academia enterprise enabling mission critical applications to be built with security and correctness. So when you see that they have such a high level here, 99% in progress, then you know that they're on the right track. So we'll keep our eye on this. And they also focus on their telegram talking about one million plus trade volume every day. You can see their, this is their Twitter page, highest yield on the Binance Smart Chain. They've got a link to their telegram, a link to their Discord, their lead developer, their CMO, and their COO. So feel free to follow all of them.  Latest buyback and burn. So they are burning tokens and that's extremely bullish. You can see total buyback and burn amount surpasses one million USD. Burn counter 9,000 NMS and buyback counter one million and 80,000 USD. You can see the January buyback reserves, 500,000 USD and current total reserves over 11 million. And we've gone through that and we showed you guys the reserves. So they have a very strong balance sheet with 11.5 million in the treasury. And I think that's extremely important. Here at RISTV Live, we like to understand the fundamentals of everything we talk about. Nemesis Dow sees 144% increase in active users. So we don't just look at the chart. We like to understand what's behind the project. And this is a project that has a treasury, which we don't see that too often. This is a project that is paying out enormous APY. And it's a project that I think is undervalued, underappreciated, underexposed. Everywhere we look, the numbers look good. This is them on GitHub. I am following them on GitHub. This is them on CoinGecko. So you can see they're listed in a lot of places here. You can see their links to BSCScan and Twitter, Telegram, Discord on your right. GitHub, everything you need to understand, the circulating supply, only 20,146. That's something I think you need to really take a focus on here. A very small circulating supply, total supply of 28,069. Market cap of only 4 million. 1.6 million in 24 hour trading volume. So pretty nice trading volume. And total locked 11.5 million in treasury. So you can learn more. And this is once again on CoinGecko. You can see the listed exchanges, Etoro, PancakeSwap, and BKEX. Once again, some of the exchanges that you can see them listed on. And it's also interesting to see how it can be at a different price on each exchange. So it's just something that you should be aware of. This is CoinMarketCap. So I always like to go to CoinMarketCap because they have a lot of good insight on coins. And they really break them down extremely well. I want you to go down here and take a look at this. So it says right here, Nemesis DAO is a decentralized finance reserve currency protocol built on the Binance Smart Chain BSC. The protocol is powered by the NMS token, which is backed by a basket of assets like BUSD and liquidity pair tokens. Nemesis DAO introduces unique dynamics into decentralized finance, otherwise known as DeFi. And we love DeFi here in RichDVLive, market through bonding and staking. So really a focus on DeFi utilizing bonding and staking to give you rewards to hold tokens. And Nemesis DAO aims to become a significant reserve currency on the Binance Smart Chain BSC, one which is truly owned and maintained by decentralized protocol, which serves the majority. Our goal is for NMS to be a common trading pair, which is backed by real assets. This is a key here, backed by real assets. You don't see that on the blockchain. So this is a company and this is a project that gets it. At a high level, Nemesis DAO consists of its protocol, managed treasury and protocol owned liquidity with staking and bonding options. As the protocol controls the funds in its treasury, NMS can only be minted or burned by the protocol. This also guarantees that the protocol can always back one NMS with one BUSD, as the protocol accumulates more PCV, more runway is guaranteed for the stakers. This means the stakers can be confident that the current staking APY can be sustained for a long term because more funds are available in the treasury. Staking is the primary value, a cruel strategy of Nemesis DAO. So the real focus of this project is staking. So I want to make sure you understand, the real focus of this project, how you're going to make money, how they plan to make money is staking the coins through staking and bonds. So be aware of that. That is the real focus. Understand what DAO is. We're here to educate you. Okay. And show you different projects. I must remind you that Rich TV Live is strictly for information and education purposes. Please do your due diligence, do your research before you invest in anything we talk about or discuss here on Rich TV Live. In saying that, we think this is a project that is undervalued, underappreciated, underexposed.  But we do think this is a project that everyone should put on their radar and put on their watch list. Okay. We love to identify early stage companies and projects that are undervalued, underappreciated, underexposed. We started talking about crypto and Bitcoin in 2017. And obviously we've seen it grow and evolve to where it is today and it is now becoming Bitcoin specifically and the cryptoverse becoming mainstream. Nemesis is now a protocol for decentralized reserve currencies on BSC. So you can see here, project overview, key advantages, high reward staking. This is what makes it unique. Huge rewards, 30 to 33% ROI for five days staking. Strong liquidity, 20% of MCAP, the market cap, treasury balance. At this time when this was done, 11.2 million, now it's over 11.5 million. The token is the NMS token as a reserve token and high trade volume daily and loyal holders. You can see here a description. Nemesis Dow is a decentralized finance reserve currency protocol built on the Binance smart chain. Protocol is powered by the NMS token and it introduces unique dynamics into the decentralized finance market through staking and bonding. You can see all the partners there. We showed you SirTik. We showed you PancakeSwap. They're also on CoinMarketCap, CoinGecko and HotBit. We showed you all of those. The NMS aspires to become an algorithmic reserve currency backed by other decentralized assets. Started with 1,000 NMS and all of it added to liquidity. You can see the roadmap for December website, UI revamp, SirTik audit, partnerships, huge buyback and burn event. And they've just continued to evolve since then. So this is the Nemesis Dow. What do you guys think of this? If you guys like it, please smash your like button. Comment down below, share the video everywhere and subscribe. You can see how they are. They are ranked in the last 24 hours. When you compare them and you look at DeFi and how you rank them, you can see here, they are ranked in the last 24 hours very, very highly and competitively. They're number one right now in the last 24 hours. Right here as far as DeFi. So this is DeFi and Dow. You can see how they rank. They're right up there in the last seven days. You can see that they rank right now fourth. In the last 30 days, you can see where they rank second on DAP radar. So this is dapradar.com and you can see that Nemesis Dow ranks really, really highly on the platform. You can see the amount of users, the daily volume, balance. And once again, breaks down what is the Nemesis Dow. Once again, focused on staking and bonds, which is essentially giving you incentives to hold the token. All right, so these are some of the key focus points and how quickly the project grew within days on CMC, CoinMarketCap and CoinGecko. And 90% of total supply will be locked as of day four. Almost 10% of the total supply was burned in day seven. One third of the team tokens burned on day seven. All of the accumulated bond fees burned on day seven. Five million market cap reached new all-time high on day 10. 6.5 million market cap reached new all-time high day 12. One million USD total team burns 559 MMS. Burned on day 13. Daily 3.5 million trade volume reached on day 13. Eight million market cap reached new all-time high broken day 14. And you can see day 15, 16, 17, 18, kept breaking new all-time highs. And then the new website went live on day 19. 17.5 million plus daily trade volume reached on day 19. Wow, that's massive. 25 million market cap reached and 20 million trade volume, 97.5% locked on day 21. So you can see all the way down to where they got CEX listed, number one on HotBit and MMS USDT pair live on day 26. And you can see the CERTEC security and audit on board day 27. 10K holders reached 10,000 holders. That's pretty impressive. Day 29. Within a month, 10,000 holders. HotBit trading caught us live on day 32. Air drop distribution daily started day 37. And daily 50K Christmas buyback and burn started day 38. Rising phase marketing campaign initiative day 39. And you can see here moving down here, coming mid-January roadmap 2022 and medium page coming January, angel nodes, new product. Stay tuned to that. Also 100 plus new YouTube videos and companies going to be featuring these guys. So be aware of that. That's a lot of visibility. That's a lot of eyeballs. So definitely something to be aware of with the nemesis Dow. And in February, they're going to be launching nemesis pro. So big updates coming in February. Stay tuned. Nemesis is burning next target. 100 million market cap on the burning phase. So once again, you can buy on pancake swap. And you can get all the information, including their price chart and coin get go coin market cap and white paper. All right. And if you want to buy on pancake swap, just click the link and it will take you right there to it. All right guys. So what do you guys think about the nemesis Dow? I think it's a cool project. Love to know what you guys think. Love to get your feedback. Nemesis Dow solves the problem of liquidity migration by owning its liquidity. This is also called DeFi 2.0. You guys, if you've been following rich TV live, you know, I absolutely love DeFi. I love decentralized finance. And I feel like it is the future of banking. And really it's essentially giving you rewards for holding your tokens through staking and bonds. So keep it simple like that. If you're really trying to understand and what is the Dow, a decentralized autonomous organization or Dow is an organization that's governed by code instead of leaders. So in the Dow, the leaders are not people. It's code. So get to know what is a Dow, understand nemesis Dow, get the highest yields on the Binance Smart Chain through the nemesis Dow, a $202 token with a tight supply and 11.5 million in treasury. Very impressive token. Once again called DeFi 2.0. Very impressed with this nemesis Dow.finance. And you can literally just go connect now or go to the legacy. And you can see their partners right there. All right, guys, what do you think you can go to click on social, see all of their social right there, click on price chart to get the price chart. We'll do that right now. And scroll down and you'll get the price chart. So everything is right there at your fingertips. This is rich from rich to be live. If you're not winning, you're probably not watching. We bring you the winners. We bring the news. We bring you the coolest newest projects in the universe. First, what do you guys think about the nemesis Dow, nemesis Dow holding liquidity inside the protocol instead of rented liquidity model where whales can take with the liquidity and leave the project after some time. Nemesis Dow solves this problem by buying liquidity from its users in exchange for discounted NMS tokens. This is why they're an absolute game changer and why I believe you need to put them on your radar on your watch list right here, right now. What do you guys think? Love to know what you guys think owning the vast majority of its liquidity nemesis Dow. Put them on your radar, put them on your watch list. I think it has a chance to be a really good project. And the nemesis Dow currently holds 99.2%. Enables a nemesis to protect the value of the reserve backing NMS tokens and to keep the protocol liquid. That allows nemesis Dow to grow its treasury further by repeating the rewards from its LP tokens, which again raises the reserve floor value as well as that of NMS. And once again, those are these bonds right here. And the BUSD, there it is. So these are the bonds, staking bonds through the nemesis Dow. This is Rich from Rich to be live and I'm out. Peace.


The COVID-19 pandemic has forced the corporate world to re-engineer how it conducts business. Eco-Carib's chief executive officer, Dinelle Flourius, who appeared on NTN's COVID-19 roadmap to recovery, youth economic empowerment, indicated that while his company had anticipated and prepared for an economic crash, the lack of financing remains a major hurdle in the survival of a business. We actually raised funding, funding round. So raising funding in the region is really, really hard, very extremely difficult. And you can go through incubators, you can go through accelerators. We've done all of this. Incubators and accelerators usually give you grants which are assigned to a certain task or for development in your business. Now, you'll still need a lot more cash to really get to the top of where you need to be to really get your product or service out there. It depends on the business that you have. I mean, I happen to choose one of the most difficult businesses in the world, which is energy. Flourius explained that a partnership with a strategic investor provided not only cash but resources and expert and technical support. Export St. Lucia's chief executive officer, Sunita Danielle, recognizing that access to financing for up-and-coming businesses can be challenging, informed that the agency has been exploring initiatives in that regard. That's one of the things that we have been really advocating for, is for a grant funding facility to be placed within Export St. Lucia for exactly that purpose. In the meantime, what we are doing is to really build those relationships with the St. Lucia Development Bank and with other commercial banks. Because one of the things we've realized is that for youth entrepreneurs, their biggest issue really is financing. It really is an issue in terms of getting any initial, like Danielle said, a lot of them have to use their savings and that can only take them as far as possible. But then afterwards, they really need that initial injection and what has been happening is that a lot of young entrepreneurs really use direct equity funding, which is not really the best for young entrepreneurs. Export St. Lucia's CEO also highlighted efforts to ensure that not only do entrepreneurs have access to financing but that they are able to honor their loans. The St. Lucia Development Bank, Export St. Lucia recently signed an MOU with them where we'd be sending young persons to get that kind of concessionary funding from St. Lucia Development Bank. But one of the conversations we've been having with them and with our commercial partners is to change the way in which young persons pay back their loans. For example, it should not be a schedule fixed repayment. These businesses really can't operate like that. What it should really be is something based on the revenues that they make on the cycle of how their revenues come in. For example, we have a lot of small, Export St. Lucia has a large clientele of small enterprises based on agriculture. And there is only the harvesting period has to come in. The planting period, then they harvest. During that time, it's not fixed on a monthly, every 28th day of the month. That's not happening. They're not getting revenues at that time, particularly when they're just beginning the businesses. And so that's a discussion we're having that you kind of amend the terms of repayment for those young startups. That was Export St. Lucia's Chief Executive Officer, Sunita Daniel.


Hi, my name is Owen Maiden. I am a graduate student in the Shirts Research Group and an open force field fellow. And today I'm going to talk to you a little bit about some of the future plans we have for looking at non-bonding interactions, particularly using Beijing approaches. So currently we've made a lot of good progress in our force field fitting as an effort as a whole. So if you look at the partially force field, it's done a lot of improvements, but this is mainly through targeted fitting of bonded interaction parameters, so bonds, angles, and torsions. One thing that we haven't really looked at a lot so far is the non-bonded interactions, so electrostatics and van der Waals interactions. So I'm going to focus today a little bit on the van der Waals, commonly used the Lennard-Jones model, but we hope to use this kind of analysis for electrostatics of the future. So the problem that we have with Lennard-Jones fitting is that, one, there's limited data, so instead of training against quantum calculations, we have to train against macroscopic observables, so we're really kind of limited to what experiments people have actually done. And also there's kind of a black box nature to the problem. So for example, if you have a van der Waals model that is parametrized with some set of parameters, so say for example Lennard-Jones is the most common, then you can get microscopic behavior from there, but then there's also other factors that go into that, like electrostatics and bonded interactions, and then from there you can run equilibrium simulations, and then you get your macroscopic properties, which is the target that you're fitting in. So there's a lot of intermediate steps here, and these intermediate steps are not really all that visible to us. We don't have a really easy understanding of if I change x parameter, then I'll get x change in macroscopic property. So in addition to this, there's also a lot of decisions that we need to make about parameters and functional forms, so there are discrete choices of functionals or models and continuous choices of parameters within those models. So for example, a discrete choice is something like the number of atom types you have in a model. Mike Gilson talked at length about this yesterday. Another thing is Lennard-Jones combining rules. So when you have those atom types, how do you actually parametrize those interactions between unlike types? And then you also have continuous choices of parameters. So for example, in your Lennard-Jones functional, you have an epsilon and a sigma, something like that. And so there's a lot of factors going on here, and what we're interested in doing is making decisions based on statistical evidence for individual models and sets of parameters. So how do we plan on doing that? And the technique that we're interested in using is called Bayesian inference. So Bayesian inference provides us a natural framework for making decisions about models. If you're unfamiliar with Bayesian inference, we like to look at posterior distributions. Costerior distributions are a combination of a likelihood distribution and a prior distribution. And so what do these really mean? Well, a prior distribution is kind of your reasonable knowledge about the model that you have before you perform your specific experiments. So you might not have all that much prior knowledge. If this isn't a model you've studied before, so you might put a pretty flat prior on what you're looking at, or if this is something that you've studied before, you might have a reasonable idea of what's going on already, and you have a more pith prior. And then from there, there's a likelihood distribution, which is talking about how well does your model and set of parameters reproduce a specific data set. So you have some new data set in terms of physical properties, and then you evaluate your model based on its performance relative to that, and then you'll get an idea of what your likelihood looks like. When you put these together, then these become the posterior distribution that combines kind of that specific likelihood with the more general prior knowledge that you already had. And so how are we going to use this to decide between models? Well, if we look at the posterior distributions and integrate under the curve, then we'll get a normalizing constant. And the normalizing constant of a posterior distribution can be thought of as model evidence. So if you take ratios of those normalizing constants ratios of that model evidence, you can interpret that as odds in favor of a model. So for example, if you have one model that has three times the normalizing constant in another model, you can interpret that as three to one odds in favor of that model. And so this is obviously a bit of a simplistic picture in a one-dimensional case, but you can imagine if you have models that are more uncertain, that have lower values for their priors, then you're going to get those models are going to be penalized. So how do we apply this specifically to the problems that we're looking at? This is going to make allow us to make data-driven choices between models. So again, if we give, for an example, if we go from a model with three atom types to a model with four atom types, similar to what Mike Gilson was looking at earlier, and we see that there is the base factor favors the model with three atom types, then we can say, well, it's not really worth it for us to add that extra atom type. It's probably better off just to stick with what we had. We're not getting a lot of extra mileage out of that extra type. And so when we're computing these, we need a way of computing these posterior distributions. And because these are like these black box problems, we can't do this analytically. We have to use some sort of Monte Carlo sampling in order to get these values out. So that process looks something like this. We'll start somewhere, propose a new set of parameter values, and then you can evaluate the posterior distribution for the set and the previous set. Then from there, you'll accept or eject the move that you make. So in this case, you accept your move, and you treat that new set of parameters as a draw from the posterior distribution. And then from there, if we do this enough times, we're able to construct posterior probability distributions. And I want to note that I have done this. I've shown this example for parameters, but it's also possible for us to do this over model space using a technique called reversible jump Monte Carlo. We can make moves between not only continuous parameter spaces, but also discrete model spaces. That's the technique that we've been using to look at the differences between normalizing constants. And so the way that we interpret that is if you're sampling both models well and you sample one 10 times more than the other model, then that model is probably 10 times better. This isn't the only way to compute the base factors, but it's what we've been using currently. You can imagine you're probably thinking about this and saying, well, if you have to run an equilibrium simulation to evaluate your model every time you do a Monte Carlo step, this isn't really going to be feasible. And I agree. And so that's why our plan is to use surrogate models to accelerate the sampling of the posterior. As I mentioned earlier, you have these intermediate steps where you get the microscopic behavior and then do a simulation. And then you get your macroscopic properties with a surrogate model. There's obviously going to be a little uncertainty out of here. But then you can go directly from your model and parameters to the macroscopic properties. So essentially, this would be an analytical function that you can put in your parameters and you can get out the response of your observable. So in order to test this strategy, we looked at a simple case study. So this is a two-center Lender-Jones plus quadrupole model. You can use this to parameterize diatomic and diatomic-like molecules. So the model has two Lender-Jones sites that have an epsilon and a sigma parameter. It also has a variable bond length. It separates those two Lender-Jones sites. And a quadrupole parameter that controls the strength of the quadrupole interaction. And because we have these different parameters, we can split this into different levels of complexity. So for example, you can say, well, I want a quadrupole parameter or I want no quadrupole interaction, have it set to zero. Or you can say, I'm going to fix my bond length, not fix my bond length. And the nice thing about this model is that there are preexisting surrogate models for density, saturation, pressure, and surface tension already developed in the literature. So we don't have to do the legwork in this case of building a surrogate model ourselves. So previously, someone took advantage of these surrogate models and performed multi-criteria optimization over the parameter space. And they produced some parameter sets for a two-criteria case. So that's just liquid density and saturation pressure and a three-criteria case that also included surface tension. And what we noticed from their data was that when you go from the two-criteria case to the three-criteria case, the quadrupole is being driven to zero. And so this begs the question, is it worth it for us to include the quadrupole parameter in this model? And we can answer this using Bayesian inference. So we ran our Bayesian inference process doing reversible Jet Money Carlo over the model space and the parameter space. And so we got some model and parameter distributions out of that. So the model distribution looks a little bit like this. It's a discrete distribution between the AUA model, which is a model without a quadrupole, and the AUA plus Q model, which is a model with a quadrupole. And what you can see here is that there are about four to one odds in favor of the model without a quadrupole. And so what this is telling us is that for this specific molecule, a quadrupole is most likely not justified. And so this level of substantial evidence that I've written here is kind of a common heuristic for Bayes factors. Substantial evidence is something like a Bayes factor, three to 20, and then strong evidence above that is 20 or higher. So this is kind of, it's a little bit arbitrary, but it's kind of a commonly used heuristic in the field. And another thing that we get out of this is we get our parameter distributions. So this is really nice information as well because we get to see the shapes of our parameter distributions. We learn a lot about the correlation between these parameters and we can even get maximum a posteriori estimates of what these parameters that should be. And if there's some sort of multimodality, like there is a little bit in this case, we can also notice that too. So we get a lot of information about our parameter distributions. So looking at this for a whole bunch of different cases and molecules, we find that in general, the quadrupole interaction is not justified. For almost all of these, the model without a quadrupole is preferred. For the one case that the quadrupole, that the model with a quadrupole is preferred, that's for acetylene in a two criteria case. In a three criteria case, it goes back to, goes back to looking for a model without a quadrupole. So this is kind of the question that we've answered here is for this case, for these molecules and these properties that we're looking at, it's probably not worth it for us to include the quadrupole. So this is a relatively simple example. And the next step is, how do we extend this past these simple model systems? So obviously in open force field, we're building biomolecular force fields. So these models are gonna be a lot more complex than the simple model I was looking at earlier. And so we need to build surrogate models. And what we really need to do when we're doing that is reduce the computational cost as much as possible. So we're looking at a multi-fidelity approach where we start with direct simulation, which is kind of our gold standard for observables, right? That's generally how we get them to serve. And then from there, we can use thermodynamic reweighting to get some information about the response in the local region of that direct simulation. And then from there, when we combine multiple direct simulation points and some thermodynamic reweighting, then on top of that, we can build an analytical model that should be quick to evaluate and do a reasonable job of approximating the response surface. And so we need simple analytical models that work well when we can only sample the true surface minimally. So obviously, because these evaluations, these equilibrium simulations are expensive, we need to do that as little as possible. And so the models that we're interested in looking at for this process or caption processes, they do well when you have minimal observations and they scale reasonably well with dimensionality. So our next challenge in learning how to build these models is how do we place our observations? What can we do to make sure that the observations that we're using are giving us the most bang for our buck? And so we need to be looking for kind of a general strategy and there's a possibility of using some gradient base methods to get us in the right regions and then start to do this. But in general, we're interested in also adapting surrogate models on the fly. So you can imagine the schema would look something like this. You start in some region, maybe far from equilibrium or maybe you used force balance to get close to close to what you think is an equilibrium value. You start sampling and eventually you move out of your model trust region which is based on the uncertainty of your model. Well, in that case, you're gonna trigger a new simulation which is you're gonna expand that trust region. And then from there, you'll continue to trigger more simulations and eventually you'll have a pretty good picture of what your high probability regions look like. So that's kind of the general strategy that we're interested in pursuing. This is something that's kind of early in the pipeline. So there's still a lot of work to be done here. But these are some of the ideas, these are some of the areas that we're gonna apply this to. So we're really interested in using this a lot for our Leonard Jones fitting but also for electrostatics in the future. We can examine and improve models for combination rules. So the commonly used Laurence Berthelot combination rules are probably a little bit simplistic. We might be able to get some extra mileage out of our Leonard Jones there, number of Smirks types. So this is kind of again, what Mike Gilson was looking at. When is it worth it to actually split off an atom type into more than one atom types? This will allow us to quantitatively decide when we're doing that. Another bigger change would be functional forms of Vanderbilt's interactions. So obviously Leonard Jones 12-6 is the most commonly used but we could also look at me potential or Buckingham potential to kind of look at that repulsive exponent. And then moving into electrostatics further down the line, we wanna look at bond charge correction schemes and make decisions between those. So with that, I'll thank you all for your attention and open it up to questions.


Yes, sir. Is that on one? Yes, sir. Do you have this? Yes, sir. Hi. Hi, Mr. President. I'm on the way to the theater when Fred got me about to the Bobby Baker State District interpretation of it. See, if you can tell me with whom the president had lunch yesterday. Okay, why don't you let me check? Well, he came and asked for money. He came and asked for help. And I told him that this is an illusion that I had all these millions and that I had told him that he should leave in 60 when I left. And he kept telling me that Mansfield had said that he had to stay on or he would leave. He just had to have him and that he had gone and sent a courier and sent a courier told him that he ought to stay on. I had told him he ought to leave and go on with all these obligations to practice law if he's ever going to practice as a youngster. And when he had these financial reverses, he came to me and asked me if I couldn't help him financially. I told him we didn't have the money, but if I were you, I'd go see Kerr. The one thing I don't remember, I'm checking my diary, is whether I telephone Kerr. I don't think that I did, but I know I told him to go see him. And I don't recall seeing him after that. I do recall Kerr seeing me and telling me that he was helping him and that he was going to see him all the way through it. And that's one thing that he felt good Lord had done for him to help him take care of his friends and not to be concerned that he was going to see his whole motel deal and take care of it. He was going to have him down to Oklahoma and work it out with his people. That's about all that I ever heard or knew about it. I never let him have a dime and didn't have it to let him have. But I always thought that Kerr did and knew he would because he was that type of fellow. And he had on many occasions told me that he was helping people up there and specifically a lot of the individuals themselves and they weren't just one party. And he liked you. He went all out for you all the way. He had done that over a period of many, many years even long before I even knew it when Mr. Aberdeen knew it. That's where I first met him before he ever even came here. But I thought it was rather improper for him to say that. But I guess if he wanted to outline the truth, as a matter of fact, they asked me to make a statement. So he did talk to one of our friends and he thought that if only one knew it and I ought to say it. We told him we couldn't do that. But that is the fact. And you know what I think about it all along. I think he's the victim of the most collusive conspiracy that I've known but be conducted by man's government. That's what I've always thought that. I think a good lord though, a good lord to put the hex on folks that do that and you just watch and see now. Some of them don't answer. Just mark them. I said that about old Dan Moody one time. I don't think that kid here, Walter Jenkins, did anything. A million people didn't do it. It was improper. But I think the way they put them in the light, the way they came under their axe, the way we sat around here and allowed them to do it, is a good lord will never forgive us for it. And I just feel ashamed. I just wish I could walk out on them. So embarrassed about my government. I don't know what to do. But until it's uncovered, it's going to be uncovered. And good lord will uncover it. This thing's not... We had a little of it in the paper the other morning, you know. But there's too much of this going on. You can just see it. I've named you a dozen of them. The same thing wherever they cross by. This is the price you pay. A week ago, I just called them. They thought I would testify to this. I said it happened and they did happen. The only thing that I see, and I'm trying to check that, and I'm not good at remembering, the only flaw that I see, and I've watched it, and I've gone over it with a trusted counsel. And I do not remember picking up the phone, but all my phone calls are diaries. And it should reflect it if I did call it. I don't think it did. I do remember him telling me he had helped him and would help him and would see him through. And I think he had done it. It had been two million. What I think happened is I think he sent him out there. And I think what this fellow did, he said, listen you sons of bitches, you've been doing awful well. We've been your friends. Now you be our friends. And he sent him out there to pick up this thing. And I think he told Mr. X, Y, and Z that I'm going to help you. Because nearly everybody went to him for help. And then I think he had a heart attack. And he committed it. Just like a good many of them commit in every election. I'm sure that there's 20 senators this last time that were told that you'll get 30, 40, 50,000. And corn spent it now, I'll get it for you. And I think that's exactly what this fellow did. And I think that he committed himself to help the senators with this money because he was doing that. And that's where he was getting a lot of his power. It goes down there. Everyone above obligated time. And I just saw Jennings Randolph last night. I remember he made the difference on one boat up there. And I thought of this. Not to anything improper at all. Because it wasn't. But he really helped him and helped him materially. Just exactly like Franklin Roosevelt helped in West Virginia too. And Junior, you remember. But those things happened. And then my judgment is the fellow got sick and never thought he was going to die. And he didn't make good on it. Or if he didn't make good, he did it another way. But I never thought this boy stole anything from him. And Nick told Abe, you never make me believe it. Then he went ahead anyway. After he said he didn't believe it himself. And at first place, I don't think you'd have to steal from Mr. Kerr. He wasn't a fellow that you had to do it for. He'd done anything he could do short of giving up his wife and his church to help this boy. He would just done it without any question. Just like your dad, he would do anything in the world he could for me. Just anything except give up his wife or something. The second thing, he wasn't smart enough to steal from Kerr. You're not even. I got hit right on both the floor. He didn't have to and he wasn't there. He wasn't a dad and he couldn't have a dog. And Kerr made no more attention to parties and anything. Boots Adams and Phillips down there is the biggest friend.  Kerr was with the Democrat. And he did the same, Kerr did the same thing in the Senate here. And he had more influence on that side than he did on his own. I think that is true. I don't think they had more. I said the one thing about it, he always puts in more than he takes out. And I think that was a fellow. I don't mention those kids no more. But the thing strange to me is that they found this mount in the box. Is there any, what's your judgment on that? But I don't have it. Wasn't that a surprise? There's some of my people that do that. That's pretty...