arXiv subjects

Vadim V. Korolev

Publications and source records attributed to Vadim V. Korolev.

8 recordsLinked to original sources

arXiv2026

Structure-Agnostic Prediction of the Electronic Density of States with a Chemical Language Model

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 cataloged. Here we introduce DOSSIER ($\textbf{D}$ensity $\textbf{o}$f $\textbf{S}$tates from $\textbf{S}$to$\textbf{i}$chiometry with $\textbf{E}$ncoder $\textbf{R}$epresentations), a chemical language model that maps elemental composition directly to this spectrum. The encoder is pretrained by cross-modal knowledge distillation from a universal machine-learning interatomic potential; the transfer lowers the error by 11% when only 1,000 training examples are available. On the Mat2Spec benchmark, DOSSIER reaches a mean absolute error of 3.76 states eV$^{-1}$ against 3.64 for the best structure-aware model; on an extended Materials Project dataset, the predicted spectra yield band gaps and $\textit{d}$-band descriptors with useful accuracy. Screening 11,977 binary and 13,251 five-component high-entropy alloy compositions for a $\textit{d}$-projected DOS resembling that of NiPt$_{3}$ places known oxygen reduction electrocatalysts near the top of the ranking.

cond-mat.mtrl-sci↗

arXiv2025

XMCQDPT2-Fidelity Transfer-Learning Potentials and a Wavepacket Oscillation Model with Power-Law Decay for Ultrafast Photodynamics

A central pursuit in theoretical chemistry is the accurate simulation of photochemical reactions, which are governed by nonadiabatic transitions through conical intersections. Machine learning has emerged as a transformative tool for constructing the necessary potential energy surfaces, but applying it to excited states faces a fundamental barrier: the cost of generating high-level quantum chemistry data. We overcome this challenge by developing machine-learning interatomic potentials (MLIPs) that achieve multi-state multi-reference perturbation theory accuracy through various techniques, such as transfer, multi-state, and $Δ$-learning. Applied to the methaniminium cation, our highest-fidelity transfer-learning model uncovers its complete photodissociation landscape following S$_2$ photoexcitation. The comprehensive XMCQDPT2/SA(3)-CASSCF(12,12) electronic structure description captures all competing decay channels, including S$_1$ branching into photoisomerization and direct H$_2$-loss pathways. Our results show that the population dynamics generally depends on the MLIP model, correlating with its performance. At the same time, the introduction of MLIP-uncertainty corrections based on the predictions of an ensemble of models brings different approaches into agreement, validating this metric as essential for reliable dynamics. To interpret the population dynamics, we introduce a wavepacket oscillation model - a mechanistically transparent, power-law kinetics framework that extracts state-specific lifetimes directly from first-principles simulations. The model quantitatively reproduces the ultrafast decay, creating a direct link between quantum transition probabilities and classical rate constants. The kinetic fits yield channel-specific lifetimes, supporting the recently discovered photochemical pathway mediated by a novel $σπ^*/S_0$ conical intersection.

physics.chem-ph↗

arXiv2025

Machine Learning Photodynamics Unveils a Controlled H$_2$ Loss Channel in Methaniminium Cation

The methaniminium cation, CH$_2$NH$_2^+$, plays an important role in Titan's N$_2$--CH$_4$ atmospheric chemistry. As the simplest protonated Schiff base (PSB), it also serves as a model for studying the nonadiabatic dynamics of retinal PSB, the chromophore central to vertebrate vision. While previous studies have established CN bond cleavage and photoisomerization as the primary pathways in the photochemistry of CH$_2$NH$_2^+$, we now report a new UV-induced photochemical pathway to HCNH$^+$, the dominant ion in Titan's upper atmosphere. Through high-level XMCQDPT2 and CASSCF(12,12) calculations, we identify a novel S$_1$/S$_0$ conical intersection that mediates the concerted double H-atom elimination from the carbon center of CH$_2$NH$_2^+$, yielding carbene CNH$_2^+$ as a direct precursor to HCNH$^+$. On-the-fly trajectory surface hopping dynamics confirm the presence of direct H$_2$ loss following excitation to either the S$_2$ or S$_1$ state. Furthermore, our large-scale, machine learning-accelerated simulations reveal that mode-specific pre-excitation can selectively funnel the dynamics into this new channel via the vibronically allowed S$_1$ state, enabling targeted control of the photochemical outcome.

physics.chem-ph↗

arXiv2022

Structure-related bandgap of hybrid lead halide perovskites and close-packed APbX3 family of phases

Metal halide perovskites APbX3 (A+ = FA+ (formamidinium), MA+ (methylammonium) or Cs+, X- = I-, Br-) are considered as prominent innovative components in nowadays perovskite solar cells. Crystallization of these materials is often complicated by the formation of various phases with the same stoichiometry but structural types deviating from perovskites such as well-known the hexagonal delta FAPbI3 polytype. Such phases are rarely placed in the focus of device engineering due to their unattractive optoelectronic properties while they are, indeed, highly important because they influence on the optoelectronic properties and efficiency of final devices. However, the total number of such phases has not been yet discovered and the complete configurational space of the polytypes and their band structures have not been studied systematically. In this work, we predicted and described all possible hexagonal polytypes of hybrid lead halides with the APbI3 composition using the group theory approach, also we analyzed theoretically the relationship between the configuration of close-packed layers in polytypes and their band gap using DFT calculations. Two main factors affecting the bandgap were found including the ratio of cubic (c) and hexagonal (h) close-packed layers and the thickness of blocks of cubic layers in the structures. We also show that the dependence of the band gap on the ratio of cubic (c) and hexagonal (h) layers in these structures are non-linear. We believe that the presence of such polytypes in the perovskite matrix might be a reason for a decrease in the charge carrier mobility and therefore it would be an obstacle for efficient charge transport causing negative consequences for the efficiency of solar cell devices.

cond-mat.mtrl-sci↗

arXiv2021

Parametrization of Non-Bonded Force Field Terms for Metal-Organic Frameworks Using Machine Learning Approach

The enormous structural and chemical diversity of metal-organic frameworks (MOFs) forces researchers to actively use simulation techniques on an equal footing with experiments. MOFs are widely known for outstanding adsorption properties, so precise description of host-guest interactions is essential for high-throughput screening aimed at ranking the most promising candidates. However, highly accurate ab initio calculations cannot be routinely applied to model thousands of structures due to the demanding computational costs. On the other side, methods based on force field (FF) parametrization suffer from low transferability. To resolve this accuracy-efficiency dilemma, we apply the machine learning (ML) approach. The trained models reproduce atom-in-material quantities, including partial charges, polarizabilities, dispersion coefficients, quantum Drude oscillator and electron cloud parameters within the accuracy of underlying density functional theory method. The aforementioned FF precursors make it possible to thoroughly describe non-covalent interactions typical for MOF-adsorbate systems: electrostatic, dispersion, polarization, and short-range repulsion. The presented approach can also significantly facilitate hybrid atomistic simulations/ML workflows.

cond-mat.mtrl-sci↗

arXiv2021

Relationships between distortions of inorganic framework and band gap of layered hybrid halide perovskites

The unprecedented structural flexibility and diversity of inorganic frameworks of layered hybrid halide perovskites (LHHPs) rise up a wide range of useful optoelectronic properties thus predetermining the extraordinary high interest to this family of materials. Nevertheless, the influence of different types of distortions of their inorganic framework on key physical properties such as band gap has not yet been quantitatively identified. We provided a systematic study of the relationships between LHHPs' band gaps and six main structural descriptors of inorganic framework, including interlayer distances (dint), in-plane and out-of-plane distortion angles in layers of octahedra (θin,θout), layer shift factor (LSF), axial and equatorial Pb-I bond distances (dax,deq). Using the set on the selected structural distortions we realized the inverse materials design based on multi-step DFT and machine learning approach to search LHHPs with target values of the band gap. The analysis of calculated descriptors band gap dependences for the wide range of generated model structures of (100) single-layered LHHPs results in the following descending order of their importance:dint > θin > dax > LSFmin > θout > deq > LSFmax, and also implies a strong interaction value for some pairs of structural descriptors. Moreover,we found that the structures with completely different distortions of inorganic framework can have similar band gap, as illustrated by a number of both experimental and model structures.

cond-mat.mtrl-sci↗

arXiv2021

Layer shift factor in layered hybrid perovskites -- univocal quantitative descriptor of composition-structure-property relationships

Asceding interest of the scientific community in layered hybrid halide perovskites (LHHPs) as materials for innovative photovoltaic and optoelectronic applications led to unprecedented expansion of this family of compounds, reaching now several hundred refined structures. Despite the unique structural diversity of LHHPs, traditional approaches of describing their structures, such as dividing into Dion-Jacobson (DJ) or Ruddlesden-Popper (RP) phases for mostt structures are ambiguous and unquantifiable. Here, we introduced a quantitative layer shift factor (LSF) for a univocal classification and quantitative comparison of the structures. We also developed an algorithm for automatic calculation of the LSF for such structures. We demonstrate the application of the proposed approach for an analysis of correlations between LSF and band gap to reveal "structure-property" relationships. Our study gives a simple and useful approach to classify of either the layered perovskite-like structures or other layered compounds composed of layers of vertex-connected octahedra as a structural unit.

cond-mat.mtrl-sci↗

arXiv2020

Database of 2D hybrid perovskite materials: open-access collection of crystal structures, band gaps and atomic partial charges predicted by machine learning

We describe a first open-access database of experimentally investigated hybrid organic-inorganic materials with two-dimensional (2D) perovskite-like crystal structure. The database includes 515 compounds, containing 180 different organic cations, 10 metals (Pb, Sn, Bi, Cd, Cu, Fe, Ge, Mn, Pd, Sb) and 3 halogens (I, Br, Cl) known so far and will be regularly updated. The database contains a geometrical and crystal chemical analysis of the structures, which are useful to reveal quantitative structure-property relationships for this class of compounds. We show that the penetration depth of spacer organic cation into the inorganic layer and M-X-M bond angles increase in the number of inorganic layers (n). The machine learning model is developed and trained on the database, for the prediction of a band gap with accuracy within 0.1 eV. Another machine learning model is trained for the prediction of atomic partial charges with accuracy within 0.01 e. We show that the predicted values of band gaps decrease with an increase of the n and with an increase of M-X-M angles for single-layered perovskites. In general, the proposed database and machine learning models are shown to be useful tools for the rational design of new 2D hybrid perovskite materials.

cond-mat.mtrl-sci↗