用机器学习的通用势函数进行分子建模
Molecular modeling with machine-learned universal potential functions
摘要：
分子模拟是药物发现中的一个重要课题。几十年的研究已经导致了高质量的可扩展分子力场的发展。在本文中，我们表明，神经网络可以用来训练一个通用的逼近能量势函数。通过将一个完全自动化的训练过程，我们已经能够训练光滑，可微，预测的大规模晶体结构的潜在功能。还进行了各种测试，以显示机器学习模型的优越性和多功能性。
英文摘要：
Molecular modeling is an important topic in drug discovery. Decades of research have led to the development of high quality scalable molecular force fields. In this paper, we show that neural networks can be used to train a universal approximator for energy potential functions. By incorporating a fully automated training process we have been able to train smooth, differentiable, and predictive potential functions on large-scale crystal structures. A variety of tests have also been performed to show the superiority and versatility of the machine-learned model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
