Category: Machine Learning in Chemistry

By Posted on Posted in Electronic Structure Calculations, Machine Learning in Chemistry, News, Semiempirical Methods No Comments Tagged with , , , , , , , , ,

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 »

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In our work published in the Journal of Chemical Physics, we investigate the performance of AIQM1 on reaction barrier heights. Our benchmark results show that, with the built-in uncertainty quantification, the accuracy of confident AIQM1 predictions outperforms its baseline ODM2* method, …

Evaluating AIQM1 on Reaction Barrier Heights Read more »

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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 »

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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 »

By Posted on Posted in Electronic Structure Calculations, Machine Learning in Chemistry, Method Development, News No Comments Tagged with , , , ,

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.

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The book “Quantum Chemistry in the Age of Machine Learning” guides aspiring beginners and specialists in this exciting field by covering topics ranging from basic concepts to comprehensive methodological details in machine learning, quantum chemistry, and their combinations in a …

Book “Quantum Chemistry in the Age of Machine Learning” Read more »

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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 »

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In the work published in the Journal of Physical Chemistry Letters, we have proposed a one-shot trajectory learning (OSTL) approach that allows an ultrafast prediction of 10-ps-long quantum dynamics of an open quantum system just in 70 milliseconds. OSTL approach takes …

One-Shot Trajectory Learning of Open Quantum Systems Dynamics Read more »