Sampling lattices in semi-grand canonical ensemble with autoregressive machine learning  

作　　者：James Damewood Daniel Schwalbe-Koda Rafael Gómez-Bombarelli 

机构地区：[1]Department of Materials Science and Engineering,Massachusetts Institute of Technology,77 Massachusetts Avenue,Cambridge,MA,02319,USA

出　　处：《npj Computational Materials》2022年第1期588-597,共10页计算材料学（英文）

基　　金：This work was supported by ARPAe DIFFERENTIATE (Award No DE-AR0001220);Zapata Computing Inc.J.D.acknowledges support from the National Defense Science and Engineering Graduate Fellowship;D.S.-K.was additionally supported by the MIT Energy Fellowship。

摘　　要：Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science.However,naive Monte Carlo approaches,on which such calculations are often dependent,struggle to scale to complex materials in many state-of-the-art disciplines such as the design of high entropy alloys or multi-component catalysts.To address this issue,we adapt sampling tools built upon machine learning-based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble.Furthermore,we show that the resulting models are transferable across wide ranges of thermodynamic conditions and can be implemented with any internal energy model U,allowing integration into many existing materials workflows.We demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd,CuAu) that exhibit diverse thermodynamic behavior in their phase diagrams.Finally,we discuss remaining challenges in model development and promising research directions for future improvements.

关 键 词：CANONICAL THERMODYNAMIC DIRECTIONS 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]