Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water
Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water
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DOI：
10.1103/physrevb.88.054104
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发表时间：
2013-08-08
影响因子：
3.7
通讯作者：
Csanyi, Gabor
中科院分区：
文献类型：
--
作者：
Bartok, Albert P.;Gillan, Michael J.;Csanyi, Gabor
我们展示了基于贝叶斯推理的机器学习技术如何用于增强分子材料的计算机模拟，重点是水。我们使用精确的相关量子化学来训练我们的机器学习算法，并预测从团簇到固相和液相的分子聚集体中的能量和力。广泛使用的电子结构方法的基础上密度泛函理论（DFT）本身给像水这样的分子材料的准确性差，我们展示了我们的技术如何可以用来产生系统的改进的一个和两个身体的DFT修正适度的额外资源。由此产生的修正DFT计划是相当准确的小水团簇和不同的冰结构的相对能量比未经修正的DFT，并显着提高了液态水的结构和动力学的描述。然而，我们对冰结构和液体的研究结果表明，超越两体DFT误差不能被忽略，我们建议如何进一步发展我们的机器学习方法来纠正这些误差。
We show how machine learning techniques based on Bayesian inference can be used to enhance the computer simulation of molecular materials, focusing here on water. We train our machine-learning algorithm using accurate, correlated quantum chemistry, and predict energies and forces in molecular aggregates ranging from clusters to solid and liquid phases. The widely used electronic-structure methods based on density functional theory (DFT) by themselves give poor accuracy for molecular materials like water, and we show how our techniques can be used to generate systematically improvable one- and two-body corrections to DFT with modest extra resources. The resulting corrected DFT scheme is considerably more accurate than uncorrected DFT for the relative energies of small water clusters and different ice structures and significantly improves the description of the structure and dynamics of liquid water. However, our results for ice structures and the liquid indicate that beyond-two-body DFT errors cannot be ignored, and we suggest how our machine-learning methods can be further developed to correct these errors.