We are happy to announce that on the occasion of its ten-year anniversary, we have released on September 12 a brand new MLatom 3 with tons of new features including more simulation options, Python API for user-customized workflows, and its …
MLatom 3: 10-year anniversary edition is released! Read more »
We have introduced a concept of 4D-spacetime atomistic AI models that learn how the molecule changes in time. We demonstrate that this concept is feasible by developing the 4D-spacetime GICnet models that directly predict the atomic coordinates of a molecule …
We improved (p)KREG models for an accurate representation of molecular potential energy surfaces (PESs) by including gradient information explicitly in their formalism. Our models are better or on par with other state-of-the-art machine learning models as we show on extensive …
(p)KREG Models for Accurate Molecular Potential Energy Surfaces Read more »
Alkyne-embedding [11]cycloparaphenylene ([11]CPPs) was functionalized with electron-donating, -neutral, and -withdrawing aryl substituents to yield a series of nanolassos via click chemistry. We used our state-of-the-art, artificial intelligence-enhanced quantum mechanical method 1 (AIQM1) to thoroughly analyze the electronic and photophysical properties of these …
Large Cycloparaphenylene Nanolassos Characterized with AIQM1 Read more »
Materials can simultaneously absorb not just one but two photons and molecules with strong two-photon absorption (TPA) are important in many fields such as unconverted laser, photodynamic therapy, and 3D printing. In our work published in Advanced Science (open access), …
Explaining and Predicting Two-Photon Absorption with Machine Learning Read more »
We are happy to announce that our brand-new manual for MLatom@XACS is now online at http://mlatom.com/manual/. Our new manual is re-designed to make it much easier to find what you need. We also greatly updated it with additional information, useful tips, …
Recently, Machine Learning (ML) is increasingly used for fast and accurate propagation of quantum dissipative dynamics including our works for the two-state system and seven-site FMO complex. The studies carried out so far demonstrated the use of different ML models …
Mario Barbatti, his group and collaborators published an update on Newton-X – a popular open-source platform for surface hopping and nuclear ensembles. An update include extension of the Newton-X platform to supervised (with our MLatom platform) and unsupervised learning (with ulamdyn). The paper is also open access and appeared in the Journal of Chemical Theory and Computation.
MLatom@XACS team introduced how to use machine learning in chemistry in the CECAM Machine Learning and Quantum Computing for Quantum Molecular Dynamics [MLQCDyn] school. This school aimed at offering state-of-the-art training in quantum molecular dynamics (QMD), machine learning (ML), and quantum computing (QC) to early-stage …
Tutorial on ML in CECAM school MLQCDyn featuring MLatom@XACS Read more »
We used our MLatom package to develop a machine learning approach for designing materials based on mixed metal halides to facilitate ammonia separation and storage.