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  MLatom: A program package for quantum chemical research assisted by machine learning

Dral, P. O. (2019). MLatom: A program package for quantum chemical research assisted by machine learning. Journal of Computational Chemistry, 40(26), 2339-2347. doi:10.1002/jcc.26004.

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 Creators:
Dral, Pavlo O.1, Author              
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1Research Department Thiel, Max-Planck-Institut für Kohlenforschung, Max Planck Society, ou_1445590              

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Free keywords: machine learning; quantum chemistry; molecular descriptor; kernel rigde regression; Fortran; Python; sampling
 Abstract: 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.

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Language(s): eng - English
 Dates: 2019-04-232019-05-312019-06-202019-10-05
 Publication Status: Published in print
 Pages: 9
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1002/jcc.26004
 Degree: -

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Title: Journal of Computational Chemistry
  Abbreviation : J. Comput. Chem.
Source Genre: Journal
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Publ. Info: New York : Wiley
Pages: - Volume / Issue: 40 (26) Sequence Number: - Start / End Page: 2339 - 2347 Identifier: ISSN: 0192-8651
CoNE: https://pure.mpg.de/cone/journals/resource/954925489848