Finding accurate solutions to the Schr\"odinger 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 8x 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.
翻译:寻找对Schr\'odinger 等式的准确解决方案是计算化学方面尚未解决的关键挑战。 鉴于它对于开发新的化学化合物的重要性,数十年的研究都致力于这一问题,但是由于巨大的多维性,甚至现有的最佳方法也还没有达到预期的准确性。最近,深层次学习和蒙特卡洛方法的结合已成为获得高度准确的能量和适度计算成本的有希望的方法。在本文件中,我们引入了一个新的深层次学习结构,在比以往方法低8x的计算成本的情况下,将能量误差降低40-70%,从而大大地为实现这一目标做出贡献。我们使用我们的方法,通过计算曾经为不同原子和分子发表的最精确的变异地面状态能量来建立一个新的基准。我们系统地分解和测量我们的改进,特别是侧重于增加物理知识的效果。我们惊讶地发现,增加先前对结构的了解,实际上会降低准确性。