论文标题
使用深度学习的Schrödinger方程的金标准解决方案：我们需要多少物理学？
Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?
论文作者
论文摘要
找到Schrödinger方程的准确解决方案是计算化学的关键尚未解决的挑战。鉴于其对新化合物的开发的重要性，数十年的研究一直致力于这个问题，但是由于较大的维度，即使是最好的可用方法，也没有达到所需的准确性。最近，深度学习与蒙特卡洛方法的结合已成为获得高度精确能量和计算成本中等规模的一种有希望的方法。在本文中，我们通过引入一种新颖的深度学习结构来为这一目标做出了重大贡献，该结构与以前的方法相比，以低于6倍的计算成本达到40-70％的能量误差。使用我们的方法，我们通过计算有史以来针对许多不同原子和分子发表的最准确的变异基态能量来建立新的基准。我们系统地分解并衡量我们的改进，尤其是在增加物理先验知识的影响上。我们出乎意料地发现，增加对体系结构的先验知识实际上可以降低准确性。
Finding accurate solutions to the Schrödinger equation is the key unsolved challenge of computational chemistry. Given its importance for the development of new chemical compounds, decades of research have been dedicated to this problem, but due to the large dimensionality even the best available methods do not yet reach the desired accuracy. Recently the combination of deep learning with Monte Carlo methods has emerged as a promising way to obtain highly accurate energies and moderate scaling of computational cost. In this paper we significantly contribute towards this goal by introducing a novel deep-learning architecture that achieves 40-70% lower energy error at 6x lower computational cost compared to previous approaches. Using our method we establish a new benchmark by calculating the most accurate variational ground state energies ever published for a number of different atoms and molecules. We systematically break down and measure our improvements, focusing in particular on the effect of increasing physical prior knowledge. We surprisingly find that increasing the prior knowledge given to the architecture can actually decrease accuracy.
