论文标题
使用机器学习与结构相互作用：进出平衡
Interaction from Structure using Machine Learning: in and out of Equilibrium
论文作者
论文摘要
在给定相互作用的经典系统的典型配置的情况下，对势的预测是困难的问题。没有确切的结果可以预测在结构信息给定潜力的情况下。我们证明，使用机器学习（ML）可以快速但准确地回答问题：哪对潜在导致给定的结构（由配对相关函数表示）？我们使用人工神经网络（NN）来解决这个问题，并表明该ML技术能够非常准确地预测对均潜在的潜在，而不论该系统是否处于晶体，液体还是气相。我们显示，训练有素的网络适用于从平衡和均衡模拟（活动物质系统）中采取的样本系统配置，当时较晚的映射到具有修改电位的有效平衡系统。我们表明，有关活动系统有效相互作用的ML预测不仅对对MIPS（运动诱导的相位分离）阶段进行预测有用，而且还可以确定向该状态的过渡。
Prediction of pair potential given a typical configuration of an interacting classical system is a difficult inverse problem. There exists no exact result that can predict the potential given the structural information. We demonstrate that using machine learning (ML) one can get a quick but accurate answer to the question: which pair potential lead to the given structure (represented by pair correlation function)? We use artificial neural network (NN) to address this question and show that this ML technique is capable of providing very accurate prediction of pair potential irrespective of whether the system is in a crystalline, liquid or gas phase. We show that the trained network works well for sample system configurations taken from both equilibrium and out of equilibrium simulations (active matter systems) when the later is mapped to an effective equilibrium system with a modified potential. We show that the ML prediction about the effective interaction for the active system is not only useful to make prediction about the MIPS (motility induced phase separation) phase but also identifies the transition towards this state.
