Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
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DOI：
10.1063/1.4964627
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发表时间：
2016-10-28
影响因子：
4.4
通讯作者：
von Lilienfeld, O. Anatole
中科院分区：
文献类型：
--
作者：
Huang, Bing;von Lilienfeld, O. Anatole
分子性质机器学习（ML）模型的预测精度取决于分子表征的选择。受量子力学假设的启发，我们引入了一种满足唯一性和目标相似性标准的表示层次。为了系统地控制目标相似性，我们简单地依赖于原子间的多体展开，就像在通用力场中实现的那样，包括键合、角化（BA）和高阶项。高阶贡献的加入系统地增加了与真实势能的相似性和所得ML模型的预测精度。我们报告了在电子相关和密度泛函理论水平上对数千个小有机分子进行预先计算的分子特性训练的BAML模型的性能的数值证据。研究的性质包括原子化焓和自由能、热容、零点振动能、偶极矩、极化率、HOMO/LUMO能和间隙、电离势、电子亲和和电子激发。经过训练，BAML以前所未有的精度和速度预测样品外分子的能量或电子特性。AIP出版社出版。
The predictive accuracy of Machine Learning (ML) models of molecular properties depends on the choice of the molecular representation. Inspired by the postulates of quantum mechanics, we introduce a hierarchy of representations which meet uniqueness and target similarity criteria. To systematically control target similarity, we simply rely on interatomic many body expansions, as implemented in universal force-fields, including Bonding, Angular (BA), and higher order terms. Addition of higher order contributions systematically increases similarity to the true potential energy and predictive accuracy of the resulting ML models. We report numerical evidence for the performance of BAML models trained on molecular properties pre-calculated at electron-correlated and density functional theory level of theory for thousands of small organic molecules. Properties studied include enthalpies and free energies of atomization, heat capacity, zero-point vibrational energies, dipole-moment, polarizability, HOMO/LUMO energies and gap, ionization potential, electron affinity, and electronic excitations. After training, BAML predicts energies or electronic properties of out-of-sample molecules with unprecedented accuracy and speed. Published by AIP Publishing.