MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913220352016384 |
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| author | Dral, Pavlo O. Ge, Fuchun Hou, Yi-Fan Zheng, Peikun Chen, Yuxinxin Barbatti, Mario Isayev, Olexandr Wang, Cheng Xue, Bao-Xin Pinheiro Jr, Max Su, Yuming Dai, Yiheng Chen, Yangtao Zhang, Lina Zhang, Shuang Ullah, Arif Zhang, Quanhao Ou, Yanchi |
| author_facet | Dral, Pavlo O. Ge, Fuchun Hou, Yi-Fan Zheng, Peikun Chen, Yuxinxin Barbatti, Mario Isayev, Olexandr Wang, Cheng Xue, Bao-Xin Pinheiro Jr, Max Su, Yuming Dai, Yiheng Chen, Yangtao Zhang, Lina Zhang, Shuang Ullah, Arif Zhang, Quanhao Ou, Yanchi |
| contents | 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 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 pre-trained 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_20155 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows Dral, Pavlo O. Ge, Fuchun Hou, Yi-Fan Zheng, Peikun Chen, Yuxinxin Barbatti, Mario Isayev, Olexandr Wang, Cheng Xue, Bao-Xin Pinheiro Jr, Max Su, Yuming Dai, Yiheng Chen, Yangtao Zhang, Lina Zhang, Shuang Ullah, Arif Zhang, Quanhao Ou, Yanchi Chemical Physics Artificial Intelligence 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 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 pre-trained 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. |
| title | MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows |
| topic | Chemical Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2310.20155 |