AI-enhanced computational chemistry
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.
We have held online broadcast on April 24, at 15:30 Beijing time/9:30 am CET on the XACS Youtube channel at https://www.youtube.com/watch?v=TOVmwgId-eA. In the broadcast, we have demonstrated how MLatom@XACS can be used for accelerating expensive quantum chemical simulations via efficient building …
A machine learning potential with low error in the potential energies does not guarantee good performance for the simulations. One of the reasons is that it is hard to train machine learning potentials with balanced descriptions of different PES regions, …
Check out our tutorial on AIQM1!
MLatom@XACS makes AI-enhanced computational chemistry more accessible and supports both ground- and excited-state simulations with quantum mechanical methods, machine learning, and their combinations. We are happy to announce that we will release the new upgraded version of MLatom 3.3.0 that …
Surface hopping dynamics with MLatom is coming: Join online broadcast! Read more »
MLatom@XACS is a powerful tool for training and using machine learning potentials. It supports a wide variety of representative potentials. These potentials include: ·Equivariant neural network MACE ·Popular ANI with a good cost/performance ratio ·Kernel methods such as KREG and …
Training and using machine learning potentials with MLatom@XACS Read more »