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 »

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 …

A comparative study of different machine learning methods for dissipative quantum dynamics Read more »

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 »

In our work published in the Journal of Physical Chemistry Letters, we investigate the performance of the general-purpose data-driven methods ANI-1ccx and AIQM1 in the calculation of enthalpies of formation. Extensive benchmark tests show that these two methods can achieve …

Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods Read more »

Our AIQM1 paper is one of the 25 most downloaded Nature Communications articles in chemistry and materials sciences published in 2021! #NCOMTop25 The full list: https://www.nature.com/collections/gagdjjgcgj AIQM1 paper: https://www.nature.com/articles/s41467-021-27340-2 How to use AIQM1 method with MLatom: http://MLatom.com/AIQM1