329 citations · 567 across the 25 of their papers we have counts for
25 papers
Pushing the limits of unconstrained machine-learned interatomic potentials
Filippo Bigi, Paolo Pegolo, Arslan Mazitov +2
Machine-learned interatomic potentials (MLIPs) are increasingly used to replace computationally demanding electronic-structure calculations to model matter at the atomic scale. The…
Giant optical anisotropy and visible-frequency epsilon-near-zero in hyperbolic van der Waals MoOCl2
Georgy Ermolaev, Adilet Toksumakov, Aleksandr Slavich +13
The realization of extreme optical anisotropy is foundational to nanoscale light manipulation. Van der Waals (vdW) crystal MoOCl2 has emerged as a promising candidate for this ques…
Record Index-Bandgap Trade-off: CdPS3 as a High-Index van der Waals Platform for Ultraviolet-Visible Nanophotonics
M. R. Povolotskiy, A. S. Slavich, G. A. Ermolaev +21
The development of nanophotonics is hindered by a fundamental trade-off between a material's refractive index (n) and its electronic bandgap (Eg), which severely restricts the choi…
Giant optical anisotropy in CrSBr from giant exciton oscillator strength
Georgy Ermolaev, Tagir Mazitov, Arslan Mazitov +18
The interplay between dimensionality and electronic correlations in van der Waals (vdW) materials offers a powerful toolkit for engineering light-matter interactions at the nanosca…
A universal machine learning model for the electronic density of states
Wei Bin How, Pol Febrer, Sanggyu Chong +5
In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbit…
Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
Filippo Bigi, Joseph W. Abbott, Philip Loche +12
Incorporation of machine learning (ML) techniques into atomic-scale modeling has proven to be an extremely effective strategy to improve the accuracy and reduce the computational c…
Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
Arslan Mazitov, Sofiia Chorna, Guillaume Fraux +4
The development of machine-learning models for atomic-scale simulations has benefited tremendously from the large databases of materials and molecular properties computed in the pa…
Giant exciton binding energy in bulk CrCl3
Georgy Ermolaev, Tagir Mazitov, Anton Minnekhanov +20
Van der Waals (vdW) materials, with their unique combination of electronic, optical, and magnetic properties, are emerging as promising platforms for exploring excitonic phenomena.…
PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
Arslan Mazitov, Filippo Bigi, Matthias Kellner +6
Machine-learning interatomic potentials (MLIPs) have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of…
Tunable Nanostructuring for van der Waals Materials
Gleb Tselikov, Anton Minnekhanov, Georgy Ermolaev +19
Van der Waals (vdW) materials are becoming increasingly popular in scientific and industrial applications because of their unique mixture of record electronic, optical, and mechani…
Substrate-aware computational design of two-dimensional materials
Arslan Mazitov, Ivan Kruglov, Alexey V. Yanilkin +5
Two-dimensional materials have attracted considerable attention due to their remarkable electronic, mechanical and optical properties, making them prime candidates for next-generat…
Analysis of stability and near-equilibrium dynamics of self-assembled Casimir cavities
Mikhail Krasnov, Arslan Mazitov, Nikita Orekhov +1
Vacuum fluctuations are a fundamental and irremovable property of a quantized electromagnetic field. These fluctuations are the cause of the Casimir effect -- mutual attraction of…
Surface segregation in high-entropy alloys from alchemical machine learning
Arslan Mazitov, Maximilian A. Springer, Nataliya Lopanitsyna +3
High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, th…
Exploring van der Waals materials with high anisotropy: geometrical and optical approaches
Aleksandr S. Slavich, Georgy A. Ermolaev, Mikhail K. Tatmyshevskiy +15
The emergence of van der Waals (vdW) materials resulted in the discovery of their giant optical, mechanical, and electronic anisotropic properties, immediately enabling countless n…
A hidden advantage of van der Waals materials for overcoming limitations in photonic integrated circuitry
Andrey A. Vyshnevyy, Georgy A. Ermolaev, Dmitriy V. Grudinin +11
With the advance of on-chip nanophotonics, there is a high demand for high refractive index, low-loss materials. Currently, this technology is dominated by silicon, but van der Waa…
Hexagonal boron nitride nanophotonics
D. V. Grudinin, G. A. Ermolaev, D. G. Baranov +10
A global trend to miniaturization and multiwavelength performance of nanophotonic devices drives research on novel phenomena, such as bound states in the continuum and Mietronics,…
Giant and tunable excitonic optical anisotropy in single-crystal CsPbX halide perovskites
G. A. Ermolaev, A. P. Pushkarev, A. Yu. Zhizhchenko +16
During the last years, giant optical anisotropy demonstrated its paramount importance for light manipulation which resulted in numerous applications ranging from subdiffraction lig…
Sr-Doped Molecular Hydrogen: Synthesis and Properties of SrH
Dmitrii V. Semenok, Wuhao Chen, Xiaoli Huang +9
Recently, several research groups announced reaching the point of metallization of hydrogen above 400 GPa. Following the mainstream of extensive investigations of compressed polyhy…
Grain boundaries in minerals: atomic structure, phase transitions, and effect on strength of polycrystals
Arslan B. Mazitov, Artem R. Oganov
Grain boundaries (GBs) and interfaces in polycrystalline materials are significant research subjects in the field of materials science. Despite a more than 50-year history of their…
Topological phase singularities in atomically thin high-refractive-index materials
Georgy Ermolaev, Kirill Voronin, Denis G. Baranov +15
Atomically thin transition metal dichalcogenides (TMDCs) present a promising platform for numerous photonic applications due to excitonic spectral features, possibility to tune the…
Evolutionary algorithm for prediction of the atomic structure of two-dimensional materials on substrates
Arslan B. Mazitov, Artem R. Oganov
Today the study of two-dimensional (2D) materials has become one of the key objectives of materials science. Unlike their three-dimensional counterparts, 2D materials can simultane…
Crystal structure prediction at finite temperatures
Ivan A. Kruglov, Alexey V. Yanilkin, Yana Propad +1
Crystal structure prediction is a central problem of theoretical crystallography and materials science, which until mid-2000s was considered intractable. Several methods, based on…
Giant optical anisotropy in transition metal dichalcogenides for next-generation photonics
G. A. Ermolaev, D. V. Grudinin, Y. V. Stebunov +17
Large optical anisotropy observed in a broad spectral range is of paramount importance for efficient light manipulation in countless devices. Although a giant anisotropy was recent…
Fast general two- and three-body interatomic potential
Sergey Pozdnyakov, Artem R. Oganov, Efim Mazhnik +2
We introduce a new class of machine learning interatomic potentials - fast General Two- and Three-body Potential (GTTP), which is as fast as conventional empirical potentials and r…