Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
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DOI：
10.1021/ct400195d
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发表时间：
2013-08-01
影响因子：
5.5
通讯作者：
Mueller, Klaus-Robert
中科院分区：
文献类型：
--
作者：
Hansen, Katja;Montavon, Gregoire;Mueller, Klaus-Robert
准确可靠地预测分子的性质通常需要计算密集的量子化学计算。最近，应用于从头计算的机器学习技术已经被提出作为用于描述分子在其整个化合物空间中的给定基态结构中的能量的有效方法（Rupp等人，Phys. Rev. Lett. 2012，108，058301）。在本文中，我们概述了一些已建立的机器学习技术，并调查的分子表示方法的性能的影响。最好的方法实现了3千卡/摩尔的各种分子的原子化能量的预测误差。在将机器学习方法应用于量子力学观测值的预测时，给出了这种性能改进的方法以及陷阱和挑战。
The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio calculations have been proposed as an efficient approach for describing the energies of molecules in their given ground-state structure throughout chemical compound space (Rupp et al. Phys. Rev. Lett. 2012, 108, 058301). In this paper we outline a number of established machine learning techniques and investigate the influence of the molecular representation on the methods performance. The best methods achieve prediction errors of 3 kcal/mol for the atomization energies of a wide variety of molecules. Rationales for this performance improvement are given together with pitfalls and challenges when applying machine learning approaches to the prediction of quantum-mechanical observables.