Machine learning is becoming increasingly more important in the field of force field development. Never has it been more vital to have chemically accurate machine learning potentials because force fields become more sophisticated and their applications expand. In this study a method for developing chemically accurate Gaussian process regression models is demonstrated for an increasingly complex set of molecules. This work is an extension to previous work showing the progression of the active learning technique in producing more accurate models in much less CPU time than ever before. The per-atom active learning approach has unlocked the potential to generate chemically accurate models for molecules such as peptide-capped glycine.

中文翻译：

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通过分子模拟的主动学习产生化学上准确的原子高斯过程回归模型

机器学习在力场开发领域变得越来越重要。拥有化学上准确的机器学习潜力从未像现在这样重要，因为力场变得更加复杂并且它们的应用得到扩展。在这项研究中，针对一组日益复杂的分子，展示了一种开发化学精确高斯过程回归模型的方法。这项工作是对先前工作的扩展，展示了主动学习技术在比以往任何时候都更短的 CPU 时间内生成更准确模型的进展。每原子主动学习方法释放了为肽封甘氨酸等分子生成化学精确模型的潜力。