Materials design with MLatom for ammonia separation and storage
We used our MLatom package to develop a machine learning approach for designing materials based on …
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Tag: ML
One-Shot Trajectory Learning of Open Quantum Systems Dynamics
In the work published in the Journal of Physical Chemistry Letters, we have proposed a one-shot trajectory …
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Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods
In our work published in the Journal of Physical Chemistry Letters, we investigate the performance of …
Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics
In the work published in Nature Communications, we have developed a blazingly fast artificial intelligence (AI)-based …
AIQM1 paper is top 25 most downloaded Nature Communications articles in chemistry and materials sciences published in 2021
Our AIQM1 paper is one of the 25 most downloaded Nature Communications articles in chemistry and …
Speeding up quantum dissipative dynamics of open systems with kernel methods
In the work published in New Journal of Physics, we combine machine learning (ML) with the …
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Artificial intelligence makes accurate quantum chemical simulations more affordable
We have developed artificial intelligence-enhanced quantum mechanical method 1 (AIQM1), which can be used out of …
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Which Machine Learning Potential to Choose?
Lost in the sea of all machine learning potentials? Our overview and recommendations based on balanced …
MLatom 2: Introducing a Platform for Atomistic Machine Learning
We are happy to introduce MLatom 2: a major release of our integrative platform for user-friendly …
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Review on Machine Learning for Molecular Excited States
In our Review “Molecular excited states through a machine learning lens” in Nature Reviews Chemistry, we provide …
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