预测凝相模拟的机器学习
Machine learning for predictive condensed-phase simulation
摘要：
我们展示了如何机器学习技术的基础上贝叶斯推断可以用来达到新的水平的现实主义的分子材料的计算机模拟，在这里集中在水。我们训练我们的机器学习算法使用准确的，相关的量子化学，并预测能量和力的分子聚集体，从集群到固相和液相。广泛使用的电子结构的方法的基础上密度泛函理论（DFT）的分子材料，如水的准确性差，我们展示了我们的技术可以用来产生系统的改进DFT的校正。由此产生的修正DFT方案给出了非常准确的预测的相对能量的小水团簇和不同的冰结构，并大大提高了描述的结构和动力学的液态水。
英文摘要：
We show how machine learning techniques based on Bayesian inference can be used to reach new levels of realism in 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) give poor accuracy for molecular materials like water, and we show how our techniques can be used to generate systematically improvable corrections to DFT. The resulting corrected DFT scheme gives remarkably accurate predictions for the relative energies of small water clusters and of different ice structures, and greatly improves the description of the structure and dynamics of liquid water.
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