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MLatom

A Package for Atomistic Simulations with Machine Learning

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Home › Blog

MLatom joins Xiamen Atomistic Computing Suite

By Pavlo Dral Posted on May 10, 2022 Posted in News No Comments

We are very happy to announce that MLatom joins Xiamen Atomistic Computing Suite (XACS) which allows us to provide much better service to the theoretical and computational chemistry community. We marked the inception of XACS by holding a local workshop …

MLatom joins Xiamen Atomistic Computing Suite Read more »

Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods

By Pavlo Dral Posted on April 18, 2022 Posted in Featured Publication No Comments
Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods

Peikun Zheng, Wudi Yang, Wei Wu, Olexandr Isayev, Pavlo O. Dral, Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods, J. Phys. Chem. Lett. 2022, 13, 3479–3491. DOI:10.1021/acs.jpclett.2c00734. Blog post › |  Tutorial ›

Speeding up quantum dissipative dynamics of open systems with kernel methods

By Fuchun Ge Posted on March 25, 2022 Posted in Featured Publication No Comments
Speeding up quantum dissipative dynamics of open systems with kernel methods

Arif Ullah, Pavlo O. Dral, Speeding up Quantum Dissipative Dynamics of Open Systems with Kernel Methods. New J. Phys. 2021, 23, 113019. DOI: 10.1088/1367-2630/ac3261. Blog post ›

AIQM1: Artificial Intelligence–Quantum Mechanical Method 1

By Pavlo Dral Posted on December 2, 2021 Posted in Uncategorized No Comments
AIQM1: Artificial Intelligence–Quantum Mechanical Method 1

Peikun Zheng, Roman Zubatyuk, Wei Wu, Olexandr Isayev, Pavlo O. Dral, Artificial Intelligence-Enhanced Quantum Chemical Method with Broad Applicability, Nat. Commun., 2021, 12, 7022. DOI: 10.1038/s41467-021-27340-2. Blog post › |  Tutorial ›

MLatom gets GUI

By Baoxin Xue Posted on September 30, 2021 Posted in Featured Publication, News No Comments
MLatom gets GUI

Tutorial and download link

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 ›

MLatom 2: Introducing a Platform for Atomistic Machine Learning

By Pavlo Dral Posted on June 16, 2021 Posted in Featured Publication, News No Comments Tagged with release
MLatom 2: Introducing a Platform for Atomistic Machine Learning

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. Read more ›

MLatom 1.2: ML absorption spectra

By Baoxin Xue Posted on November 20, 2020 Posted in Featured Publication, News No Comments Tagged with release
MLatom 1.2: ML absorption spectra

Bao-Xin Xue, Mario Barbatti, Pavlo O. Dral, Machine Learning for Absorption Cross Sections, J. Phys. Chem. A 2020, 124, 7199–7210. DOI: 10.1021/acs.jpca.0c05310. Read more ›

Chapter on Machine Learning in Quantum Chemistry in a Tutorial Way

By Pavlo Dral Posted on September 22, 2020 Posted in Uncategorized No Comments
Chapter on Machine Learning in Quantum Chemistry in a Tutorial Way

Pavlo O. Dral, Quantum Chemistry Assisted by Machine Learning. In Advances in Quantum Chemistry: Chemical Physics and Quantum Chemistry, Volume 81, 1st ed.; Kenneth Ruud, Erkki J. Brändas, Eds. Academic Press: 2020; Vol. 81. Read more ›

MLatom 1.1

By Pavlo Dral Posted on March 30, 2020 Posted in News 2 Comments Tagged with release
MLatom 1.1

MLatom 1.1 is released. Read more ›

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News & Posts

  • MLatom joins Xiamen Atomistic Computing Suite
  • Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods
  • Speeding up quantum dissipative dynamics of open systems with kernel methods
  • AIQM1: Artificial Intelligence–Quantum Mechanical Method 1
  • MLatom gets GUI

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