↓ Skip to Main Content

MLatom

AI-enhanced computational chemistry

Main Navigation

  • Home
  • Aitomic
  • A-MLatom and MLatom@XACS
  • Documentation
    • 中文
  • News
  • About
  • Contact
    • Privacy Statement
    • Cookie Policy

Finally! Periodic boundary conditions in MLatom

By Pavlo Dral Posted on July 31, 2024 Posted in Uncategorized

We have been asked countless times when MLatom can support the periodic boundary conditions (PBC). Finally, we have released the new version of MLatom supporting the calculations with periodic boundary conditions! To get it, you can either use MLatom on …

Finally! Periodic boundary conditions in MLatom Read more »

Molecular Raman spectra simulations online!

By Yifan Hou Posted on July 25, 2024 Posted in Uncategorized

Molecular Raman spectra can now be calculated online with MLatom on the XACS cloud computing platform* using a simple input file: *As of 24 July 2024, only for GFN2-xTB and UAIQM with GFN2-xTB baseline. You can find the frequencies and …

Molecular Raman spectra simulations online! Read more »

Directly learning molecular dynamics!

By Pavlo Dral Posted on July 17, 2024 Posted in Uncategorized

Molecules are always in motion.This motion can be simulated with molecular dynamics approach but it needs many steps to evaluate and is very slow. Just to demonstrate the pain of propagating MD let’s do it by hand for a couple …

Directly learning molecular dynamics! Read more »

Simulating ambimodal reactions online!

By Pavlo Dral Posted on July 11, 2024 Posted in Uncategorized

Chemistry students learn early that a reaction proceeds from reactants to products via a transition state connecting them. This simple picture does not always hold though. The reactions never pass exactly through the transition state and we must propagate molecular dynamics trajectories …

Simulating ambimodal reactions online! Read more »

Active learning for building data-efficient machine learning potentials

By Pavlo Dral Posted on July 11, 2024 Posted in Uncategorized

If you want to use machine learning for potential energy surfaces, one of the biggest obstacles is getting the data to train machine learning potential. We have recently developed the physics-informed active learning protocol for efficient data sampling and training …

Active learning for building data-efficient machine learning potentials Read more »

Supercharge your computational chemistry with the universal and updatable AI models

By Pavlo Dral Posted on June 27, 2024 Posted in Uncategorized

Choosing a quantum chemical method suitable for your simulations is not an easy task, because you need to balance accuracy andcomputational cost requirements. Unless you use B3LYP all the time, of course. Generally, the more time you spend, the more …

Supercharge your computational chemistry with the universal and updatable AI models Read more »

Molecular IR spectra simulations online!

By Yifan Hou Posted on June 19, 2024 Posted in Uncategorized

Molecular IR spectra can now be calculated online with MLatom@XACS with DFT using a simple input file: You can find the frequencies and the corresponding intensities in the output file: You can check out the online tutorial for the theoretical …

Molecular IR spectra simulations online! Read more »

JCTC: Surface hopping dynamics with QM and ML methods

By Pavlo Dral Posted on June 12, 2024 Posted in News Tagged with JCTC, MLatom, publication
JCTC: Surface hopping dynamics with QM and ML methods

XACS team in collaboration with Mario Barbatti and groups in Warsaw University and Zhejiang lab has recently published a paper in JCTC about the versatile Python implementation of surface-hopping dynamics. This implementation is based on a powerful MLatom ecosystem for …

JCTC: Surface hopping dynamics with QM and ML methods Read more »

DFT calculations online on XACS cloud

By Pavlo Dral Posted on June 5, 2024 Posted in Uncategorized

Want to run DFT calculations in an easy way? Search no more! You can easily run DFT calculations online on XACS cloud with simple inputs such as this for geometry optimization. We also provide extensive online tutorials guiding you through various types …

DFT calculations online on XACS cloud Read more »

Transfer learning for better AI models with less data

By Pavlo Dral Posted on May 29, 2024 Posted in Uncategorized

Transfer learning (TL) is an often-used technique in machine learning that helps you train better AI models. It was used to create accurate universal ML models for computational chemistry – AIQM1 (Nat. Commun. 2021, 12, 7022) and ANI-1ccx (Nat. Commun. …

Transfer learning for better AI models with less data Read more »

Posts pagination

Previous 1 2 3 4 5 … 9 Next
Copyright © 2025 Dr. Pavlo O. Dral | Powered by Responsive Theme