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 mixed metal halides to facilitate ammonia separation and storage.
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 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 …
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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 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 …
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 quantum dynamics (QD) approach with applications to excitation energy transfer in the well-known Fenna–Matthews–Olson (FMO) complex found in green sulfur bacteria.
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 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
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 numerically exact hierarchical equations of motion (HEOM) approach, propagating quantum dynamics of a two-state system (spin-boson model) with only ca. 10% of the HEOM …
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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 the box for very fast quantum chemical calculations with the accuracy of the gold-standard coupled-cluster method. Read more »
Which Machine Learning Potential to Choose?
Lost in the sea of all machine learning potentials? Our overview and recommendations based on balanced analysis are just out in Chemical Science. In brief, kernel methods are a better choice for not too large data in terms of both …
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 atomistic machine learning. It includes many more features and is further optimized for efficiency. Detailed overview of MLatom 2 is given in our contribution …
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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 insights and highlight challenges of the rapidly growing field of machine learning for excited-states simulation and analysis.