1 citations · 1 across the 4 of their papers we have counts for
4 papers
Structure-Agnostic Prediction of the Electronic Density of States with a Chemical Language Model
Ivan D. Rubtsov, Ivan V. Dudakov, Vadim V. Korolev
The electronic density of states (DOS) is conventionally computed from a relaxed crystal structure, which is unavailable for compounds that have been neither synthesized nor catalo…
XMCQDPT2-Fidelity Transfer-Learning Potentials and a Wavepacket Oscillation Model with Power-Law Decay for Ultrafast Photodynamics
Ivan V. Dudakov, Pavel M. Radzikovitsky, Dmitry S. Popov +5
A central pursuit in theoretical chemistry is the accurate simulation of photochemical reactions, which are governed by nonadiabatic transitions through conical intersections. Mach…
Machine Learning Photodynamics Unveils a Controlled H Loss Channel in Methaniminium Cation
Daniil N. Chistikov, Pavel M. Radzikovitsky, Dmitry S. Popov +4
The methaniminium cation, CHNH, plays an important role in Titan's N--CH atmospheric chemistry. As the simplest protonated Schiff base (PSB), it also serves as a…
Enhancing composition-based materials property prediction by cross-modal knowledge transfer
Ivan Rubtsov, Ivan Dudakov, Yuri Kuratov +1
Crystal graph neural networks are widely applicable in modeling experimentally synthesized compounds and hypothetical materials with unknown synthesizability. In contrast, structur…