AI-enhanced computational chemistry
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 »
We are happy to announce that MLatom 3.2.0 is released on 19.03.2024. See the full release notes for details. How to get it? Download zip, check this version on PyPI and GitHub. pip install mlatom==3.2.0 What’s new? This is a major release with many new features, …
My review ‘AI in computational chemistry through the lens of a decade-long journey’ was published open access as an invited Feature Article in Chemical Communication. It gives a perspective on the progress of AI tools in computational chemistry through the …