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MLatom : A program package for quantum chemical research assisted by machine learning
Author(s) -
Dral Pavlo O.
Publication year - 2019
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.26004
Subject(s) - computer science , fortran , scripting language , generalization , kernel (algebra) , computational science , oak ridge national laboratory , computation , machine learning , theoretical computer science , algorithm , parallel computing , programming language , artificial intelligence , mathematics , physics , combinatorics , nuclear physics , mathematical analysis
MLatom is a program package designed for computationally efficient simulations of atomistic systems with machine‐learning (ML) algorithms. It can be used out‐of‐the‐box as a stand‐alone program with a user‐friendly online manual. The use of MLatom does not require extensive knowledge of machine learning, programming, or scripting. The user need only prepare input files and choose appropriate options. The program implements kernel ridge regression and supports Gaussian, Laplacian, and Matérn kernels. It can use arbitrary, user‐provided input vectors and can convert molecular geometries into input vectors corresponding to several types of built‐in molecular descriptors. MLatom saves and re‐uses trained ML models as needed, in addition to estimating the generalization error of ML setups. Various sampling procedures are supported and the gradients of output properties can be calculated. The core part of MLatom is written in Fortran, uses standard libraries for linear algebra, and is optimized for shared‐memory parallel computations. © 2019 Wiley Periodicals, Inc.