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

npj Computational Materials 2022年第1期8卷 588-597页

作者： James Damewood daniel schwalbe-koda Rafael Gómez-Bombarelli Department of Materials Science and Engineering Massachusetts Institute of Technology77 Massachusetts AvenueCambridgeMA02319USA

Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials ***,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 *** 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 ***,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 *** demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd,CuAu) that exhibit diverse thermodynamic behavior in their phase ***,we discuss remaining challenges in model development and promising research directions for future improvements.

关键词： canonical thermodynamic directions

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Learning Matter:Materials Design with Machine Learning andAtomistic Simulations

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Accounts of Materials Research 2022年第3期3卷 343-357页

作者： Simon Axelrod daniel schwalbe-koda Somesh Mohapatra James Damewood Kevin P.Greenman Rafael Gómez-Bombarelli Department of Chemistry and Chemical Biology Harvard UniversityCambridgeMassachusetts 02138United States Department of Materials Science and Engineering Massachusetts Institute of TechnologyCambridgeMassachusetts 02139United States

CONSPECTUS:Designing new materials is vital for addressing pressing societal challenges in health,energy,and *** combination of physicochemical laws and empirical trial and error has long guided material design,but this approach is limited by the cost of experiments and the difficulty of deriving complex guiding *** space of hypothetical materials to be considered is incredibly large,and only a small fraction of possible compounds can ever be tested *** computational techniques of atomistic simulation and machine learning(ML)offer an avenue to rapidly invent new materials and navigate this enormous ***,they can be used to infer complex design principles and identify high-quality candidates more rapidly than trial-and-error *** this Account,we review our group’s recent contributions to simulation and ML for materials *** begin by discussing the numerical representation of materials for use in *** can be produced through deterministic algorithms,learnable encodings,or physics-based methods and lead to vector,graph,and matrix *** describe how these different approaches offer distinct material-and application-specific *** provide demonstrations from our own work on small-molecule drugs,macromolecules,dyes,electrolytes,and *** several cases,we show how the appropriate representation led to guiding principles that facilitated experimental materials ***,we highlight the development of ML methods for enhancing atomistic *** advances help to improve simulation accuracy and expand the time and length scales that can be *** include differentiable atomistic simulations in which ensemble-averaged quantities are differentiated with respect to system parameters,and novel autoregressive methods for enhanced sampling of challenging physical *** developments include learnable coarse-grained models,which can accelerate molecula

关键词： zeolite differentiable latter

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