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.
Our editorial on the Special Topic “Modern semiempirical methods” was published in JCP. We overview contributions and overview the trends in the development and applications of semiempirical quantum mechanical methods. The contributions include improvements in the formalism of the method …
Editorial on the Special Topic “Modern semiempirical methods” published in JCP Read more »
The new MLatom 3 release comes with the versatile Python API. We are happy to announce the release of its documentation which is available at http://mlatom.com/docs.
The 2nd edition of the International Symposium on Machine Learning in Quantum Chemistry, SMLQC 2023, is approaching and will be held from November 29 to December 1 in Uppsala, Sweden. See the symposium’s website https://www.smlqc2023.com/ for more information. The deadline …
SMLQC 2023: The 2nd International Symposium on Machine Learning in Quantum Chemistry Read more »
We are happy to announce that on the occasion of its ten-year anniversary, we have released on September 12 a brand new MLatom 3 with tons of new features including more simulation options, Python API for user-customized workflows, and its …
MLatom 3: 10-year anniversary edition is released! Read more »
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 …
Surging efforts and fast progress in AI methods for photochemistry and photophysics make it difficult to track the current state of the art. We cover the recent developments in this field in the chapter on Machine learning methods in photochemistry …
Chapter “Machine Learning Methods in Photochemistry and Photophysics” Read more »
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 »