AI-enhanced computational chemistry
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 …
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 …
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 »
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 »
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 »
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 …
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 »
MLatom supports a wide range of universal machine learning (ML)-based models including ML potentials and hybrid ML-enhanced quantum mechanical (QM) methods. They can be used out-of-box without training. In short, we have there several new universal (no training needed) ML …
Easy-to-use universal AI models for modern computational chemistry Read more »
Quasi-classical molecular dynamics (also known as quasi-classical trajectories (QCT)) accounts for the zero-point energy (ZPE) in contrast to classical dynamics and is very popular in studying chemical reactions (see, e.g., works by Houk et al. in PNAS 2012, 109, 12860 and J. Am. …
Quasi-classical trajectories to study reaction mechanisms like in PNAS and JACS papers! Read more »
MLatom 3.4.0 has been released on 29.03.2024. This release is a major release with usability improvements: Contributed to this release: Pavlo O. Dral, Yuxinxin Chen, Fuchun Ge, and Yi-Fan Hou.