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Article Dans Une Revue Journal of Chemical Theory and Computation Année : 2024

MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows

Pavlo O Dral
Fuchun Ge
Yi-Fan Hou
Peikun Zheng
Yuxinxin Chen
  • Fonction : Auteur
Olexandr Isayev
Cheng Wang
Bao-Xin Xue
  • Fonction : Auteur
Max Pinheiro Jr
Yuming Su
  • Fonction : Auteur
Yiheng Dai
  • Fonction : Auteur
Yangtao Chen
  • Fonction : Auteur
Lina Zhang
Shuang Zhang
  • Fonction : Auteur
Arif Ullah
Quanhao Zhang
Yanchi Ou
  • Fonction : Auteur

Résumé

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.
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Origine : Publication financée par une institution
Licence : CC BY - Paternité

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hal-04430655 , version 1 (01-02-2024)

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Paternité

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Pavlo O Dral, Fuchun Ge, Yi-Fan Hou, Peikun Zheng, Yuxinxin Chen, et al.. MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows. Journal of Chemical Theory and Computation, In press, ⟨10.1021/acs.jctc.3c01203⟩. ⟨hal-04430655⟩
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