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MLatom

A Package for Atomistic Simulations with Machine Learning

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Home › Posts tagged publication

Tag: publication

(p)KREG Models for Accurate Molecular Potential Energy Surfaces

By Yifan Hou Posted on June 1, 2023 Posted in News No Comments Tagged with JCTC, KREG, KRR, ML, MLatom, MLP, PES, publication
(p)KREG Models for Accurate Molecular Potential Energy Surfaces

Yi-Fan Hou, Fuchun Ge, Pavlo O. Dral. Explicit Learning of Derivatives with the KREG and pKREG Models on the Example of Accurate Representation of Molecular Potential Energy Surfaces. J. Chem. Theory Comput. 2023, 19 (8), 2369–2379. DOI: 10.1021/acs.jctc.2c01038. Blog post ›

WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets

By Pavlo Dral Posted on March 13, 2023 Posted in Featured Publication, News No Comments Tagged with manual, publication
WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets

Max Pinheiro Jr*†, Shuang Zhang†, Pavlo O. Dral, Mario Barbatti*. WS22 database: combining Wigner Sampling and geometry interpolation towards configurationally diverse molecular datasets. Sci. Data 2023, 10, 95. DOI: 10.1038/s41597-023-01998-3. Blog post ›

Explaining and Predicting Two-Photon Absorption with Machine Learning

By Pavlo Dral Posted on February 14, 2023 Posted in Featured Publication No Comments Tagged with manual, publication
Explaining and Predicting Two-Photon Absorption with Machine Learning

Yuming Su, Yiheng Dai, Yifan Zeng, Caiyun Wei, Yangtao Chen, Fuchun Ge, Peikun Zheng, Da Zhou*, Pavlo O. Dral*, Cheng Wang*. Interpretable Machine Learning of Two-Photon Absorption. Adv. Sci. 2023, 2204902. DOI: 10.1002/advs.202204902. Blog post ›

Evaluating AIQM1 on Reaction Barrier Heights

By Yaohuang Huang Posted on February 10, 2023 Posted in Featured Publication No Comments Tagged with manual, publication
Evaluating AIQM1 on Reaction Barrier Heights

Yuxinxin Chen, Yanchi Ou, Peikun Zheng, Yaohuang Huang, Fuchun Ge, Pavlo Dral*. Benchmark of General-Purpose Machine Learning-Based Quantum Mechanical Method AIQM1 on Reaction Barrier Heights. J. Chem. Phys. 2023, in press. DOI: 10.1063/5.0137101. Blog post ›

Benchmarking machine learning potentials

By Fuchun Ge Posted on September 24, 2021 Posted in Featured Publication, News No Comments Tagged with publication, release
Benchmarking machine learning potentials

Max Pinheiro Jr, Fuchun Ge, Nicolas Ferré, Pavlo O. Dral, Mario Barbatti. Choosing the right molecular machine learning potential. Chem. Sci., 2021, 12, 14396–14413. DOI: 10.1039/D1SC03564A. Blog post › | Tutorial ›

News & Posts

  • (p)KREG Models for Accurate Molecular Potential Energy Surfaces
  • WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets
  • Explaining and Predicting Two-Photon Absorption with Machine Learning
  • Evaluating AIQM1 on Reaction Barrier Heights
  • Roundup of MLatom’s Year 2022. What to Expect in 2023?

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