Releases

This is legacy page, check the new page: http://mlatom.com/docs/releases.html.

Current release is MLatom, version 3.1.1.

Version 3.1

Released on 12.29.2023.

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Citation (to be updated):

Pavlo O. Dral, MLatom: A Program Package for Quantum Chemical Research Assisted by Machine Learning. J. Comput. Chem. 2019, 40, 2339–2347. DOI: 10.1002/jcc.26004.

Pavlo O. Dral, Fuchun Ge, Bao-Xin Xue, Yi-Fan Hou, Max Pinheiro Jr, Jianxing Huang, Mario Barbatti, MLatom 2: An Integrative Platform for Atomistic Machine Learning. Top. Curr. Chem. 2021, 379, 27. DOI: 10.1007/s41061-021-00339-5.

to be updated: Pavlo O. Dral, Peikun Zheng, Bao-Xin Xue, Fuchun Ge, Yi-Fan Hou, Max Pinheiro Jr, Yuming Su, Yiheng Dai, Yangtao Chen, MLatom: A Package for Atomistic Simulations with Machine Learning, version 3.0.0. Xiamen University, Xiamen, China, 2013–2023. http://MLatom.com.

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What’s new?

  • New features:
    • MACE interface

Minor releases

  • MLatom 3.1.1 (19.01.2024)
    • bug fixes
    • performance improvements
MLatom_v3.1.0Download
MLatom_v3.1.1Download

Version 3.0

Released on 12.09.2023.

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Citation (to be updated):

Pavlo O. Dral, MLatom: A Program Package for Quantum Chemical Research Assisted by Machine Learning. J. Comput. Chem. 2019, 40, 2339–2347. DOI: 10.1002/jcc.26004.

Pavlo O. Dral, Fuchun Ge, Bao-Xin Xue, Yi-Fan Hou, Max Pinheiro Jr, Jianxing Huang, Mario Barbatti, MLatom 2: An Integrative Platform for Atomistic Machine Learning. Top. Curr. Chem. 2021, 379, 27. DOI: 10.1007/s41061-021-00339-5.

to be updated: Pavlo O. Dral, Peikun Zheng, Bao-Xin Xue, Fuchun Ge, Yi-Fan Hou, Max Pinheiro Jr, Yuming Su, Yiheng Dai, Yangtao Chen, MLatom: A Package for Atomistic Simulations with Machine Learning, version 3.0.0. Xiamen University, Xiamen, China, 2013–2023. http://MLatom.com.

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What’s new?

  • New features:
    • Python API.
    • Molecular dynamics.
    • ML-accelerated quantum dynamics.
    • IR and power spectra from MD.
    • QM methods.

Minor releases

  • MLatom 3.0.1 (13.11.2023)
    • Bug fixes.
    • Better in-code documentation.
    • Supported version of DFT-D4 changed to 3.5.0.
MLatom_v3.0.1Download

Version 2.3

Released on 15.12.2022.

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Citation:

Pavlo O. Dral, MLatom: A Program Package for Quantum Chemical Research Assisted by Machine Learning. J. Comput. Chem. 2019, 40, 2339–2347. DOI: 10.1002/jcc.26004.

Pavlo O. Dral, Fuchun Ge, Bao-Xin Xue, Yi-Fan Hou, Max Pinheiro Jr, Jianxing Huang, Mario Barbatti, MLatom 2: An Integrative Platform for Atomistic Machine Learning. Top. Curr. Chem. 2021, 379, 27. DOI: 10.1007/s41061-021-00339-5.

Pavlo O. Dral, Peikun Zheng, Bao-Xin Xue, Fuchun Ge, Yi-Fan Hou, Max Pinheiro Jr, Yuming Su, Yiheng Dai, Yangtao Chen, MLatom: A Package for Atomistic Simulations with Machine Learning, version 2.3.3. Xiamen University, Xiamen, China, 2013–2022. http://MLatom.com.

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What’s new?

  • New features:
    • implemented learning XYZ gradients explicitly by KREG and pKREG model (preprint).
    • implemented machine learning two-photon absorption (preprint).

Minor releases

  • MLatom 2.3.3 (2022-12-15) – implementation of ML-TPA (Pavlo O. Dral, Peikun Zheng, Bao-Xin Xue, Fuchun Ge, Yi-Fan Hou, Max Pinheiro, Yuming Su, Yiheng Dai, Yangtao Chen, Jr, MLatom: A Package for Atomistic Simulations with Machine Learning, version 2.3.3. Xiamen University, Xiamen, China, 2013–2022. http://MLatom.com.)
  • MLatom 2.3.2 (2022-10-19) – implementation of KREG (Pavlo O. Dral, Peikun Zheng, Bao-Xin Xue, Fuchun Ge, Yi-Fan Hou, Max Pinheiro Jr, MLatom: A Package for Atomistic Simulations with Machine Learning, version 2.3.2. Xiamen University, Xiamen, China, 2013–2022. http://MLatom.com.)
MLatom_v2.3.3Download
MLatom_v2.3.2Download

Version 2.2

Released on 18.04.2022.

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Citation:

Pavlo O. Dral, MLatom: A Program Package for Quantum Chemical Research Assisted by Machine Learning. J. Comput. Chem. 2019, 40, 2339–2347. DOI: 10.1002/jcc.26004.

Pavlo O. Dral, Fuchun Ge, Bao-Xin Xue, Yi-Fan Hou, Max Pinheiro Jr, Jianxing Huang, Mario Barbatti, MLatom 2: An Integrative Platform for Atomistic Machine Learning. Top. Curr. Chem. 2021, 379, 27. DOI: 10.1007/s41061-021-00339-5.

Pavlo O. Dral, Peikun Zheng, Bao-Xin Xue, Fuchun Ge, Yi-Fan Hou, Max Pinheiro Jr, MLatom: A Package for Atomistic Simulations with Machine Learning, version 2.2. Xiamen University, Xiamen, China, 2013–2022. http://MLatom.com.

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What’s new?

  • New features:
    • heats of formation with ANI-1ccx
    • more kernel functions for KRR: periodic kernel (keywords: kernel=Gaussian periodKernel period=R, where R is a user-defined period) and decaying periodic kernel (keywords: kernel=Gaussian decayKernel period=R sigmap=S, where R is a user-defined period and S is a user-defined length scale for a periodic part). Other keywords are the same as for the KRR with the Gaussian kernel function
    • other improvements and bug fixes

Minor releases

  • MLatom 2.2.1 (2022-10-18) – documentation update (periodic and decaying periodic kernel)
MLatom_v2.2.1-1Download
MLatom_v2.2Download

Version 2.1

Released on 02.12.2021.

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Citation:

Pavlo O. Dral, MLatom: A Program Package for Quantum Chemical Research Assisted by Machine Learning. J. Comput. Chem. 2019, 40, 2339–2347. DOI: 10.1002/jcc.26004.

Pavlo O. Dral, Fuchun Ge, Bao-Xin Xue, Yi-Fan Hou, Max Pinheiro Jr, Jianxing Huang, Mario Barbatti, MLatom 2: An Integrative Platform for Atomistic Machine Learning. Top. Curr. Chem. 2021, 379, 27. DOI: 10.1007/s41061-021-00339-5.

Pavlo O. Dral, Peikun Zheng, Bao-Xin Xue, Fuchun Ge, Yi-Fan Hou, Max Pinheiro Jr, MLatom: A Package for Atomistic Simulations with Machine Learning, version 2.1. Xiamen University, Xiamen, China, 2013–2021. http://MLatom.com.

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What’s new?

  • New features:
    • AIQM1 method
    • geometry optimizations
    • frequency calculations

Version 2.0

Released on 05.05.2021.

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Tutorial

Citation:

Pavlo O. Dral, MLatom: A Program Package for Quantum Chemical Research Assisted by Machine Learning. J. Comput. Chem. 2019, 40, 2339–2347. DOI: 10.1002/jcc.26004.

Pavlo O. Dral, Fuchun Ge, Bao-Xin Xue, Yi-Fan Hou, Max Pinheiro Jr, Jianxing Huang, Mario Barbatti, MLatom 2: An Integrative Platform for Atomistic Machine Learning. Top. Curr. Chem. 2021, 379, 27. DOI: 10.1007/s41061-021-00339-5.

Pavlo O. Dral, Bao-Xin Xue, Fuchun Ge, Yi-Fan Hou, Max Pinheiro Jr, MLatom: A Package for Atomistic Simulations with Machine Learning, version 2.0. Xiamen University, Xiamen, China, 2013–2021. http://MLatom.com.

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What’s new?

  • New features:
    • analytical gradients for Gaussian and Matern kernels, KREG and Coulomb matrix.
    • interfaces to third-party software implementing popular models (sGDML, GAP-SOAP, PhysNet, ANI, DeepPot-SE)
    • hyperparameter optimization with the hyperopt package
    • learning curves
    • learning on both energies and gradients by third-party models
    • much more efficient calculation of spectra with ML-NEA approach
    • much better help (MLatom.py help)

Minor releases

  • MLatom 2.0.5 (2021-12-02) – bug fix in ML-NEA (failed to extend the nuclear ensemble)
  • MLatom 2.0.4 (2021-10-05) – added MLatom GUI, bug fix in ML-NEA (ignored eq.xyz)
  • MLatom 2.0.3 (2021-09-10) – added source code of MLatomF under license CC BY-NC-ND 4.0
  • MLatom 2.0.1 (2021-06-09) – citation update & bug fixes
MLatom 2.0.5Download
MLatom 2.0.4Download
MLatom 2.0.3Download

Version 1.2

Released on 20.11.2020.

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Citation:

Pavlo O. Dral, MLatom: A Program Package for Quantum Chemical Research Assisted by Machine Learning. J. Comput. Chem. 2019, 40, 2339–2347. DOI: 10.1002/jcc.26004.

Pavlo O. Dral, Bao-Xin Xue, Fuchun Ge, Yi-Fan Hou, MLatom: A Package for Atomistic Simulations with Machine Learning, version 1.2. Xiamen University, Xiamen, China, 2013–2020. http://MLatom.com.

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What’s new?

  • Change to Python 3: The python part of MLatom was upgraded to Python 3.6+.
  • New features:
    • cross section: MLatom can significantly accelerate the calculation of cross-section with the Nuclear Ensemble Approach (NEA). In brief, this feature uses fewer QC calculation to achieve higher precision and reduce computational cost. You can find more detail on this paper (please cite it when using this feature:
    • The KREG model (Kernel-ridge-regression using RE descriptor and the Gaussian kernel function) as default. Default hyperparameter optimization options were updated to get better the hyperparameters for KREG. (lgOptDepth=3, NlgSigma=6, lgLambdaL=-35)
    • minor bugs fixed

Version 1.1

Released on 30.03.2020.

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Citation:

Pavlo O. Dral, MLatom: A Program Package for Quantum Chemical Research Assisted by Machine Learning. J. Comput. Chem.2019, 40, 2339–2347. DOI: 10.1002/jcc.26004.

Pavlo O. Dral, MLatom: A Package for Atomistic Simulations with Machine Learning, version 1.1. Xiamen University, Xiamen, China, 2013–2020. http://MLatom.com.

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What’s new?

  • Tutorial: Online tutorial is now available with examples how to use MLatom. It will be updated regularly based on FAQ received from you and students. It also shows examples from real studies so that the interested user can try their hand on reproducing published research.
  • New features:
    • input file: now if only one option is given to MLatom, it will consider it to be a name of the input file. Options can be given in one or several lines of the input file. Example of a typical command is: mlatom input_file.inp > output.out
    • Δ-machine learning: now you can perform Δ-machine learning [J. Chem. Theory Comput. 2015, 11, 2087] by providing MLatom with the values of the baseline method.
    • structure-based sampling from the sliced data: structure-based sampling can be now performed from data sliced into regions as described in J. Chem. Phys. 2017, 146, 244108.
    • leave-one-out cross-validation… (LOOCV) is implemented for both model evaluation and selection. LOOCV is a special type of k-fold cross-validation, when all the points are re-used for testing/validating, i.e. the number of cross-validation splits is equal to the total number of points.
      • …for model evaluation: The new keyword LOOtest requests leave-one-out cross-validation (LOOCV) for model evaluation
      • …for model selection: The new keyword LOOopt requests leave-one-out cross-validation (LOOCV) for hyperparameter optimization.
    • the exponential kernel function: The exponential kernel function can be now requested with the special option kernel=exponential.
    • debug print:
      • Printing the regression coefficients α when using the ML model.
      • Much other additional information is printed for other tasks.
  • Miscellaneous:
    • The default type of sampling keyword is set to random (in previous versions, the default type was none).
    • Self-correction option extended to any file name with reference data.

Version 1.0

Released on 19.04.2019.

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Citation:

Pavlo O. Dral, MLatom: A Program Package for Quantum Chemical Research Assisted by Machine Learning. J. Comput. Chem.2019, 40, 2339–2347. DOI: 10.1002/jcc.26004.

Pavlo O. Dral, MLatom: A Package for Atomistic Simulations with Machine Learning, version 1.0. Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr, Germany, 2013–2019. http://MLatom.com.

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What’s new?

  • Added wrapper python routine MLatom.py for high-level tasks
  • Much faster code for training kernel ridge regression models
  • New features:
    • permutationally invariant kernel
    • sorted variant of RE molecular descriptor (vector {req/r})
    • self-correction scheme
    • option for saving XYZ geometries with atoms sorted by nuclear repulsions
    • options for providing names of files with user-defined indices of various subsets
  • Bug fixes:
    • testing and training indices generated and saved to text files with sample sampling=random options (i.e. when no ML calculations were performed per se) were not randomly sampled. This bug did not affect ML operations.
    • In case of CVtest option and hyperparameter optimization , if the user requested to save files with indices for sub-training and validation sets, these files were generated only for one combination of cross-validation parts.

Version 0.92, revision 102

Released on 07.10.2018.

Features

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Citation:

Pavlo O. Dral, MLatom: A Package for Atomistic Simulations with Machine Learning, developmental version 0.92, revision 102. Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr, Germany, 2013–2018. http://MLatom.com.

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