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.

通过使用适用于氧含有机化合物的贝叶斯委员会机器学习方法，该研究探讨了蛋白质相互作用以及蛋白质和有机化合物的粘合亲和性的预测模型，并通过系统测试将其定位为一种有潜力的全能学习机器。

含氧有机化合物的贝叶斯委员会机潜力

A Bayesian Committee Machine Potential for Oxygen-containing Organic  Compounds

We have developed a technique combining the accuracy of quantum Monte Carlo
in describing the electron correlation with the efficiency of a Machine
Learning Potential (MLP). We use kernel regression in combination with SOAP
(Smooth Overlap of Atomic Position) features, implemented here in a very
efficient way. The key ingredients are: i) a sparsification technique, based on
farthest point sampling, ensuring generality and transferability of our MLPs
and ii) the so called $\Delta$-learning, allowing a small training data set, a
fundamental property for highly accurate but computationally demanding
calculations, such as the ones based on quantum Monte Carlo. As the first
application we present a benchmark study of the liquid-liquid transition of
high-pressure hydrogen and show the quality of our MLP, by emphasizing the
importance of high accuracy for this very debated subject, where experiments
are difficult in the lab, and theory is still far from being conclusive.

发展了一种结合了量子蒙特卡罗的准确性与机器学习潜能的效率的技术，使用核回归与 SOAP 特征，并利用基于最远点采样的稀疏技术和 Δ-learning，可以将训练数据集保持小，以实现高精度计算，第一个应用涉及高压氢的液 - 液相转变的基准研究，并强调了高精度的重要性，以提高实验结果不明确领域的准确性。