A Bayesian Committee Machine Potential for Oxygen-containing Organic
  Compounds
Seungwon Kim D. ChangMo Yang Soohaeng Yoo Willow Chang Woo Myung
Understanding the pivotal role of oxygen-containing organic compounds in
serving as an energy source for living organisms and contributing to protein
formation is crucial in the field of biochemistry. This study addresses the
challenge of comprehending protein-protein interactions (PPI) and developing
predicitive models for proteins and organic compounds, with a specific focus on
quantifying their binding affinity. Here, we introduce the active Bayesian
Committee Machine (BCM) potential, specifically designed to predict
oxygen-containing organic compounds within eight groups of CHO. The BCM
potential adopts a committee-based approach to tackle scalability issues
associated with kernel regressors, particularly when dealing with large
datasets. Its adaptable structure allows for efficient and cost-effective
expansion, maintaing both transferability and scalability. Through systematic
benchmarking, we position the sparse BCM potential as a promising contender in
the pursuit of a universal machine learning potential.