Theoretical IR (infrared) spectroscopy is a powerful tool for assisting chemical structure identification. However, approaches based on quantum chemical calculations suffer from either high computational cost (e.g., density functional theory, DFT) or insufficient accuracy (semi-empirical methods). Hence, we introduce a new …
ML-enhanced Fast and Interpretable Simulation of IR Spectra Read more »
Density functional theory (DFT) methods are by far the most popular approaches for electronic structure calculations. However, the “best” functional remains elusive despite the increasing variety of functionals and continuous efforts to improve their computational accuracy. In our work published in Advanced …
Adv. Sci.: The Best DFT Functional Is the Ensemble of Functionals Read more »
Recently, we published a paper in JCTC about the end-to-end physics-informed active learning with data-efficient construction of machine learning potentials. It shortens molecular simulation time to a couple of days which could have taken weeks of pure quantum chemical calculations.
The work “Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics” performed in collaboration of Professor Pavlo O. Dral (Xiamen University) and Assistant Professor Arif Ullah (Anhui University) was published in Digital Discovery. In this blog, Arif Ullah highlights this …
Physically-consistent quantum dissipative dynamics simulations with neural networks Read more »
A year ago, we released MLatom 3, making MLatom the fully-fledged Python package. This shift endowed MLatom with much of its current flexibility that also allowed us to rapidly do method development and new implementations: in one year we had …
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 »
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, …
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 »
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 …