AI-accelerated nonadiabatic dynamics reduces the cost of the ab initio simulations of nonlinear time-resolved spectra. We have developed a robust protocol and demonstrated its feasibility for calculating stimulated emission contributions in transient absorption pump–probe and 2D electronic spectra of pyrazine. …
The capabilities of MLatom 3 are described in the paper published in the J. Chem. Theory Comput. which was converted into an interactive online manual. See also the video overview.
Molecular dynamics simulations are widely used to study molecules and materials and lots of effort is put into making these simulations obey the physical laws. Energy conservation law is obviously one of the most important laws that MD should respect. …
Energy-conserving molecular dynamics is not energy conserving! 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 …
In a recent article published in Frontiers in Physics, we introduce QD3SET-1, a database consisting of 8 data sets that provide the time-evolved population and coherence dynamics for two widely studied systems: the so-called spin-boson model and FMO complex. The …
QD3SET-1: A Database with Quantum Dissipative Dynamics Data Sets 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 …
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.
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.
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 »