People

@article{WOS:001711023100001,
title = {Artificial Intelligence for Multiscale Modeling in Solid-State Physics
and Chemistry: A Comprehensive Review},
author = {Artem Maevskiy and Vitalii Kapitan and Andrey Ustyuzhanin},
doi = {10.1002/aisy.202501219},
times_cited = {0},
year = {2026},
date = {2026-03-01},
journal = {ADVANCED INTELLIGENT SYSTEMS},
publisher = {WILEY-V C H VERLAG GMBH},
address = {POSTFACH 101161, 69451 WEINHEIM, GERMANY},
abstract = {Recent progress in artificial intelligence (AI) has transformed
methodologies across many areas of science. In materials research, AI
has enabled more efficient multiscale modeling by linking atomic,
mesoscale, and continuum scales with improved accuracy and reduced
computational cost. This review examines AI-based approaches in this
context and discusses their relationship to conventional analytical and
computational multiscale methods. Developments such as machine learning
force fields, graph neural networks, and AI-accelerated electronic
structure prediction are assessed with respect to their capabilities and
limitations. To illustrate the current state of the art in this field,
available software, computational tools, and benchmarks are discussed.
Applications in areas such as phase transitions, defect dynamics, and
bulk property prediction are shown, with an emphasis on how AI enhances
predictive capabilities. While highlighting the above-mentioned recent
advances, existing challenges and promising directions are also
discussed. This review is intended for two audiences: For AI
researchers, it demonstrates how physical and chemical constraints
influence models' development to ensure physical consistency, and for
physicists, chemists, and materials scientists, it illustrates how AI
can improve multiscale methods to solve previously inaccessible problems},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{WOS:001633477300003,
title = {Inverse design of broadband antennas for terahertz devices based on
two-dimensional materials},
author = {M Y Lukianov and A Maevskiy and N Kazeev and D Mylnikov and D A Svintsov and K S Novoselov and A Ustyuzhanin and D A Bandurin},
doi = {10.1103/gr2z-3qjp},
times_cited = {0},
issn = {2331-7019},
year = {2025},
date = {2025-11-01},
journal = {PHYSICAL REVIEW APPLIED},
volume = {24},
number = {5},
publisher = {AMER PHYSICAL SOC},
address = {ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA},
abstract = {Terahertz technology, a cornerstone of next-generation high-speed
communication and sensing, has long been hindered by impedance mismatch
challenges that limit device performance and applicability. These
challenges become particularly pronounced when ultrasensitive
two-dimensional (2D) materials are employed as the detecting element in
the terahertz range, further complicating their integration in realworld
applications. Furthermore, conventional antenna designs often fail to
provide adequate matching across the broad terahertz spectrum. In this
work, we tackle these challenges using a procedural generation algorithm
to design terahertz broadband antennas that satisfy specific performance
criteria. Namely, the developed inverse design methodology enables
customization for the target impedance value, bandwidth, and contact
topology requirements. The proposed antenna achieves an improvement of
up to 40% in power transfer efficiency, compared with traditional
bow-tie antennas, under realistic operating conditions. High-fidelity
electromagnetic simulations validate these results, confirming the
design's practicality for terahertz applications. This work addresses
critical limitations of existing antenna designs and advances the
feasibility of high-frequency applications in both communication and
sensing.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{WOS:001547472300004,
title = {Time-local stochastic equation of motion for solid ionic electrolytes},
author = {A Rodin and B A Olsen and A Ustyuzhanin and A Maevskiy},
doi = {10.1103/jnzr-q953},
times_cited = {0},
year = {2025},
date = {2025-08-01},
journal = {PHYSICAL REVIEW RESEARCH},
volume = {7},
number = {3},
publisher = {AMER PHYSICAL SOC},
address = {ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA},
abstract = {Numerical studies of ionic motion through solid electrolytes commonly
involve static nudged-elastic band methods or costly ab initio molecular
dynamics. Building on a time-local model of current carrier-electrolyte
interaction and incorporating thermal motion, we introduce an approach
that is intermediate between the two well-established methodologies by
treating the electrolyte as an effective medium that interacts with the
mobile particle. Through this coupling, the thermally vibrating
electrolyte imparts energy to the charge carriers while also absorbing
energy from them due to its own finite elasticity. Using a simple model
system, we validate our approach through a series of numerical
simulations. Our methodology reproduces both dissipative and diffusive
behavior and helps link microscopic system parameters to measurable
macroscopic properties.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{WOS:001493752600014,
title = {Predicting ionic conductivity in solids from the machine-learned
potential energy landscape},
author = {Artem Maevskiy and Alexandra Carvalho and Emil Sataev and Volha Turchyna and Keian Noori and Aleksandr Rodin and Castro A H Neto and Andrey Ustyuzhanin},
doi = {10.1103/PhysRevResearch.7.023167},
times_cited = {9},
year = {2025},
date = {2025-05-01},
journal = {PHYSICAL REVIEW RESEARCH},
volume = {7},
number = {2},
publisher = {AMER PHYSICAL SOC},
address = {ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA},
abstract = {Discovering new superionic materials is essential for advancing
solid-state batteries, which offer improved energy density and safety
compared to traditional lithium-ion batteries with liquid electrolytes.
Conventional computational methods for identifying such materials are
resource-intensive and not easily scalable. Recently, universal
interatomic potential models have been developed using equivariant graph
neural networks. These models are trained on extensive datasets of
first-principles force and energy calculations. One can achieve
significant computational advantages by leveraging them as the
foundation for traditional methods of assessing the ionic conductivity,
such as molecular dynamics or nudged elastic band techniques. However,
the generalization error from model inference on diverse atomic
structures arising in such calculations can compromise the reliability
of the results. In this work, we propose an approach for the quick and
reliable screening of ionic conductors through the analysis of a
universal interatomic potential. Our method incorporates a set of
heuristic structure descriptors that effectively employ the rich
knowledge of the underlying model while requiring minimal generalization
capabilities. Using our descriptors, we rank lithium-containing
materials in the Materials Project database according to their expected
ionic conductivity. Eight out of the ten highest-ranked materials are
confirmed to be superionic at room temperature in first-principles
calculations. Notably, our method achieves a speed-up factor of
approximately 50 compared to molecular dynamics driven by a
machine-learning potential, and it is at least 3000 times faster
compared to first-principles molecular dynamics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{WOS:001310575500018,
title = {Local-time formula for dissipation in solid ionic electrolytes},
author = {A Rodin and B A Olsen and A Ustyuzhanin and A Maevskiy and K Noori},
doi = {10.1103/PhysRevResearch.6.033244},
times_cited = {2},
year = {2024},
date = {2024-09-01},
journal = {PHYSICAL REVIEW RESEARCH},
volume = {6},
number = {3},
publisher = {AMER PHYSICAL SOC},
address = {ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA},
abstract = {When ions move through solids, they interact with the solid's
constituent atoms and cause them to vibrate around their equilibrium
points. This vibration, in turn, modifies the potential landscape
through which the mobile ions travel. Because the present-time potential
depends on past interactions, the coupling is inherently nonlocal in
time, making its numerical and analytical treatment challenging. For
sufficiently slow-moving ions, we linearize the phonon spectrum to show
that these nonlocal effects can be ignored, giving rise to a draglike
force. Unlike the more familiar drag coefficient in liquids, the drag
takes on a matrix form due to the crystalline structure of the
framework. We numerically simulate trajectories and dissipation rates
using both the time-local and nonlocal formulas to validate our
simplification. The time-local formula dramatically reduces the
computational cost of calculating the motion of a mobile particle
through a crystalline framework and clearly connects the properties of
the material to the drag experienced by the particle.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}