arXiv subjects

Aleksey N. Kolmogorov

Publications and source records attributed to Aleksey N. Kolmogorov.

At least 19 recordsLinked to original sources

arXiv2026

Performance of universal machine learning potentials in global optimization of inorganic crystal structures

Rapid development of universal machine learning potentials (uMLPs) and expansion of training data sets are reshaping the state of the art in atomistic simulation, highlighting the need for concurrent systematic benchmarking of their capabilities. Global optimization is among the most demanding uMLP applications because unconstrained exploration includes probing motifs not present in reference sets. We examined twelve pretrained uMLPs in unconstrained evolutionary searches to assess whether these models can consistently predict complex nonmagnetic crystal structure ground states under ambient pressure across diverse inorganic systems. Our findings demonstrate that the considered M3GNet, MACE-MATPES, MACE-mh-1, SevenNet, EquiformerV2, EquiformerV3, MatterSim, GRACE, eSEN, Orb-v3, PET-MAD, and PET-OAM models span a wide performance range, from near {\it ab initio} to essentially non-predictive, in their ability to resolve competing phases within low-energy basins. Additional tests on hcp-Zn, MB$_4$ (M = Cr, Mn, and Fe), and LiB$_{y}$ ($y\approx 0.9$) ground states reveal that several uMLPs capture fine energy differences arising from subtle electronic structure features.

cond-mat.mtrl-sci↗

arXiv2025

EPW-VASP interface for first-principles calculations of electron-phonon interactions

We present an interface between the Vienna \textit{Ab initio} Simulation Package (VASP) and the EPW software for calculating materials properties governed by electron-phonon (e-ph) interactions. Computation of the e-ph matrix elements with the finite-difference supercell approach in VASP and their fine-grid interpolation in EPW enable accurate modeling of temperature-dependent materials properties and phonon-assisted quantum processes with VASP's extensive library of exchange-correlation functionals and pseudopotentials. We demonstrate the functionality of the EPW-VASP interface by examining the superconducting gap and critical temperature in MgB$_2$ using the anisotropic Migdal-Eliashberg equations, and the carrier mobility in cubic BN using the \textit{ab initio} Boltzmann transport equation.

cond-mat.mtrl-sci↗

arXiv2025

Rigid muffin-tin approximation in plane-wave codes for fast modeling of phonon-mediated superconductors

We present a pseudopotential-based plane-wave implementation of the rigid muffin-tin approximation (RMTA), offering a computationally efficient alternative to its traditional use in all-electron codes. This approach enables the evaluation of angular-momentum-resolved electron-phonon matrix elements and McMillan-Hopfield parameters of not only elemental transition metals but also their compounds. The results are benchmarked against full-potential linearized augmented plane wave calculations, showing excellent agreement. We further outline a practical route to extract atom- and symmetry-type-resolved electron-phonon coupling constants. By enabling the use of RMTA descriptors within high-throughput workflows, this framework significantly lowers the computational cost of screening candidate superconductors, providing a valuable tool for materials discovery.

cond-mat.supr-con↗

arXiv2025

High-$T_{\rm c}$ Ag$_x$BC and Cu$_x$BC superconductors accessible via topochemical reactions

Hole-doping of covalent materials has long served as a blueprint for designing conventional high-$T_{\rm c}$ superconductors, but thermodynamic constraints severely limit the space of realizable compounds. Our ab initio results indicate that metastable Ag$_x$BC and Cu$_x$BC phases can be accessed via standard topochemical ion exchange reactions starting from Li$_x$BC precursors. Unlike all known stoichiometric layered metal borocarbides, the predicted AgBC and CuBC derivatives, comprising honeycomb layers bridged by dumbbells, are metallic rather than semiconducting. Anisotropic Migdal-Eliashberg analysis reveals that the intrinsically hole-doped AgBC possesses a unique combination of electronic and vibrational features to exhibit two-gap superconductivity above 50 K.

cond-mat.supr-con↗

arXiv2025

Improving structure search with hyperspatial optimization and TETRIS seeding

Advanced structure prediction methods developed over the past decades include an unorthodox strategy of allowing atoms to displace into extra dimensions. A recently implemented global optimization of structures from hyperspace (GOSH) has shown promise in accelerating the identification of global minima on potential energy surfaces defined by simple interatomic models. In this study, we extend the GOSH formalism to more accurate Behler-Parrinello neural network (NN) potentials, make it compatible with efficient local minimization algorithms, and test its performance on nanoparticles and crystalline solids. For clusters modeled with NN potentials, four-dimensional optimization offers fairly modest improvement in navigating geometric relaxation pathways and incurs increased computational cost largely offsetting the benefit, but it provides a significant advantage in facilitating atom swaps in nanoalloys. In comparison, the introduction of a moderate, controlled bias for generating more physically sensible starting configurations, achieved via TETRIS-inspired packing of atomic blocks, has a more direct impact on the efficiency of global structure searches. The benchmarked systems are Lennard-Jones clusters, Au or Cu-Pd-Ag nanoparticles and binary Sn alloys described by NN potentials, and compounds with covalent B or BC frameworks modeled with density functional theory

cond-mat.mtrl-sci↗

arXiv2024

Stability-superconductivity map for compressed Na-intercalated graphite

A recent ab initio investigation of Na-C binary compounds under moderate pressures has uncovered a possible stable NaC$_4$ superconductor with an estimated critical temperature up to 41K. We revisit this promising binary system by performing a more focused exploration of Na-intercalated graphite configurations, assessing the sensitivity of their thermodynamic stability to density functional approximations at different (T,P) conditions, and examining their superconducting properties with the anisotropic Migdal-Eliashberg formalism. The combinatorial screening of possible Na arrangements reveals additional stable stoichiometries, i.e., Na$_3$C$_{10}$, NaC$_8$, NaC$_{10}$, and NaC$_{12}$, that redefine the previously proposed convex hulls for pressures up to 10 GPa. The evaluation of formation enthalpies with different van der Waals functionals indicates that the proposed compounds might not be thermodynamically stable at zero temperature but some of them could stabilize due to the vibrational entropy or form via cold compression if graphite is used as a starting material. Our more rigorous modeling of the electron-phonon coupling in NaC$_4$ confirms the material's potential for high-temperature superconductivity, with a critical temperature reaching 48 K at 10 GPa, and reveals a well-defined two-gap structure unusual for an electron-doped compound. By tracking the position of the intercalant nearly free electron states with respect to the Fermi level in viable Na-C compounds, we map out the range of pressures and compositions needed for strong electron-phonon coupling and identify Na$_3$C$_{10}$ as an equally promising superconductor.

cond-mat.supr-con↗

arXiv2024

First-principles design of ambient-pressure Mg$_x$B$_2$C$_2$ and Na$_x$BC superconductors

We employ ab initio modeling to investigate the possibility of attaining high-temperature conventional superconductivity in ambient-pressure materials based on the known MgB$_2$C$_2$ and recently proposed thermodynamically stable NaBC ternary compounds. The constructed $(T,P_M)$ phase diagrams (M = Mg or Na) indicate that these layered metal borocarbides can be hole-doped via thermal deintercalation that has been successfully used in previous experiments to produce Li$_y$BC ($y=1>x\gtrsim0.5$) samples. The relatively low temperature threshold required to trigger NaBC desodiation may help prevent the formation of defects shown recently to be detrimental to the electron-phonon coupling in the delithiated LiBC analog. According to our numerical solutions of the anisotropic full-bandwidth Migdal-Eliashberg equations, the proposed Mg$_x$B$_2$C$_2$ and Na$_x$BC materials exhibit superconducting critical temperatures between 43 K and 84 K. At the same time, we demonstrate that buckling of defect-free honeycomb BC layers, favored in heavily-doped Na$_x$BC compounds, can substantially reduce or effectively suppress the materials' potential for MgB$_2$-type superconductivity.

cond-mat.supr-con↗

arXiv2024

Prospect of high-temperature superconductivity in layered metal borocarbides

Delithiation of the known layered LiBC compound was predicted to induce conventional superconductivity at liquid nitrogen temperatures but extensive experimental work over the past two decades has detected no signs of the expected superconducting transition. Using a combination of first-principles stability analysis and anisotropic Migdal-Eliashberg formalism, we have investigated possible Li$_x$BC morphologies and established what particular transformations of the planar honeycomb BC layers are detrimental to the material's superconductivity. We propose that Li$_x$BC reintercalation with select alkali and alkaline earth metals could lead to synthesis of otherwise inaccessible metastable Li$_x$M$_y$BC superconductors with critical temperatures ($T_{c}$) up to 73 K. The large-scale exploration of metal borocarbides has revealed that NaBC and Li$_{1/2}$Na$_y$BC layered phases are likely true ground states at low temperatures. The findings indicate that this compositional space may host overlooked synthesizable compounds with potential to break the $T_{c}$ record for conventional superconductivity in ambient-pressure materials.

cond-mat.supr-con↗

arXiv2023

Machine learning search for stable binary Sn alloys with Na, Ca, Cu, Pd, and Ag

We present our findings of a large-scale screening for new synthesizable materials in five M-Sn binaries, M = Na, Ca, Cu, Pd, and Ag. The focus on these systems was motivated by the known richness of M-Sn properties with potential applications in energy storage, electronics packaging, and superconductivity. For the systematic exploration of the large configuration space, we relied on our recently developed MAISE-NET framework that constructs accurate neural network interatomic potentials and utilizes them to accelerate ab initio global structure searches. The scan of over two million candidate phases at a fraction of the typical ab initio calculation cost has uncovered 29 possible intermetallics thermodynamically stable at different temperatures and pressures (1 bar and 20 GPa). Notable predictions of ambient-pressure materials include a simple hP6-NaSn$_2$ phase, fcc-based Pd-rich alloys, tI36-PdSn$_2$ with a new prototype, and several high-temperature Sn-rich ground states in the Na-Sn, Cu-Sn, and Ag-Sn systems. Our modeling work also involved ab initio (re)examination of previously observed M-Sn compounds that helped explain the entropy-driven stabilization of known Cu-Sn phases. The study demonstrates the benefits of guiding structure searches with machine learning potentials and significantly expands the number of predicted thermodynamically stable crystalline intermetallics achieved with this strategy so far.

cond-mat.mtrl-sci↗

arXiv2023

Thermodynamic stability of Li-B-C compounds from first principles

Prediction of high-$T_{\rm{c}}$ superconductivity in hole-doped Li$_x$BC two decades ago has brought about an extensive effort to synthesize new materials with honeycomb B-C layers, but the thermodynamic stability of Li-B-C compounds remains largely unexplored. In this study, we use density functional theory to characterize well-established and recently reported Li-B-C phases. Our calculation of the Li chemical potential in Li$_x$BC helps estimate the ($T$,$P$) conditions required for delithiation of the LiBC parent material, while examination of B-C phases helps rationalize the observation of metastable BC$_3$ polymorphs with honeycomb and diamond-like morphologies. At the same time, we demonstrate that recently reported BC$_3$, LiBC$_3$, and Li$_2$B$_2$C phases with new crystal structures are both dynamically and thermodynamically unstable. With a combination of evolutionary optimization and rational design, we identify considerably more natural and favorable Li$_2$B$_2$C configurations that, nevertheless, remain above the thermodynamic stability threshold.

cond-mat.mtrl-sci↗

arXiv2022

$Ab~initio$ study of Li-Mg-B superconductors

LiB, a predicted layered compound analogous to the MgB$_2$ superconductor, has been recently synthesized via cold compression and quenched to ambient pressure, yet its superconducting properties have not been measured. According to prior isotropic superconductivity calculations, the critical temperature ($T_{\rm{c}}$) was expected to be only 10$-$15 K. Using the anisotropic Migdal-Eliashberg formalism, we show that the $T_{\rm{c}}$ may actually exceed 32 K. Our analysis of the contribution from different electronic states helps explain the detrimental effect of pressure and doping on the compound's superconducting properties. In the search for related superconductors, we screened Li-Mg-B binary and ternary layered materials and found metastable phases with $T_{\rm{c}}$ close to or even 10$-$20% above the record 39 K value in MgB$_2$. Our reexamination of the Li-B binary phase stability reveals a possible route to synthesize the LiB superconductor at lower pressures readily achievable in multianvil cells.

cond-mat.supr-con↗

arXiv2022

Prediction of stable Li-Sn compounds: boosting ab initio searches with neural network potentials

The Li-Sn binary system has been the focus of extensive research because it features Li-rich alloys with potential applications as battery anodes. Our present re-examination of the binary system with a combination of machine learning and ab initio methods has allowed us to screen a vast configuration space and uncover a number of overlooked thermodynamically stable alloys. At ambient pressure, our evolutionary searches identified a new stable Li$_3$Sn phase with a large BCC-based hR48 structure and a possible high-T LiSn$_4$ ground state. By building a simple model for the observed and predicted Li-Sn BCC alloys we constructed an even larger viable hR75 structure at an exotic 19:6 stoichiometry. At 20 GPa, new 11:2, 5:1, and 9:2 phases found with our global searches destabilize previously proposed phases with high Li content. The findings showcase the appreciable promise machine learning interatomic potentials hold for accelerating ab initio prediction of complex materials.

cond-mat.mtrl-sci↗

arXiv2021

Inhomogeneous Kondo-lattice in geometrically frustrated Pr$_{2}$Ir$_{2}$O$_{7}$

Magnetic fluctuations induced by geometric frustration of local Ir-spins disturb the formation of long range magnetic order in the family of pyrochlore iridates, R$_{2}$Ir$_{2}$O$_{7}$ (R = lanthanide)$^{1}$. As a consequence, Pr$_{2}$Ir$_{2}$O$_{7}$ lies at a tuning-free antiferromagnetic-to-paramagnetic quantum critical point and exhibits a diverse array of complex phenomena including Kondo effect, biquadratic band structure, metallic spin-liquid (MSL), and anomalous Hall effect$^{2-5}$. Using spectroscopic imaging with the scanning tunneling microscope, complemented with machine learning K-means clustering analysis, density functional theory, and theoretical modeling, we probe the local electronic states in single crystal of Pr$_{2}$Ir$_{2}$O$_{7}$ and discover an electronic phase separation. Nanoscale regions with a well-defined Kondo resonance are interweaved with a non-magnetic metallic phase with Kondo-destruction. Remarkably, the spatial nanoscale patterns display a correlation-driven fractal geometry with power-law behavior extended over two and a half decades, consistent with being in proximity to a critical point. Our discovery reveals a new nanoscale tuning route, viz. using a spatial variation of the electronic potential as a means of adjusting the balance between Kondo entanglement and geometric frustration.

cond-mat.str-el↗

arXiv2020

MAISE: Construction of neural network interatomic models and evolutionary structure optimization

Module for ab initio structure evolution (MAISE) is an open-source package for materials modeling and prediction. The code's main feature is an automated generation of neural network (NN) interatomic potentials for use in global structure searches. The systematic construction of Behler-Parrinello-type NN models approximating ab initio energy and forces relies on two approaches introduced in our recent studies. An evolutionary sampling scheme for generating reference structures improves the NNs' mapping of regions visited in unconstrained searches, while a stratified training approach enables the creation of standardized NN models for multiple elements. A more flexible NN architecture proposed here expands the applicability of the stratified scheme for an arbitrary number of elements. The full workflow in the NN development is managed with a customizable 'MAISE-NET' wrapper written in Python. The global structure optimization capability in MAISE is based on an evolutionary algorithm applicable for nanoparticles, films, and bulk crystals. A multitribe extension of the algorithm allows for an efficient simultaneous optimization of nanoparticles in a given size range. Implemented structure analysis functions include fingerprinting with radial distribution functions and finding space groups with the SPGLIB tool. This work overviews MAISE's available features, constructed models, and confirmed predictions.

physics.comp-ph↗

arXiv2017

The AFLOW Fleet for Materials Discovery

The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computational materials design automates high-throughput first principles calculations, and provides tools for data verification and dissemination for a broad community of users. AFLOW incorporates different computational modules to robustly determine thermodynamic stability, electronic band structures, vibrational dispersions, thermo-mechanical properties and more. The AFLOW data repository is publicly accessible online at aflow.org, with more than 1.7 million materials entries and a panoply of queryable computed properties. Tools to programmatically search and process the data, as well as to perform online machine learning predictions, are also available.

cond-mat.mtrl-sci↗

arXiv2017

A numerical study of Gibbs $u$-measures for partially hyperbolic diffeomorphisms on $\mathbb T^3$

We consider a hyperbolic automorphism $A\colon\mathbb T^3\to\mathbb T^3$ of the 3-torus whose 2-dimensional unstable distribution splits into weak and strong unstable subbundles. We unfold $A$ into two one-parameter families of Anosov diffeomorphisms --- a conservative family and a dissipative one. For diffeomorphisms in these families we numerically calculate the strong unstable manifold of the fixed point. Our calculations strongly suggest that the strong unstable manifold is dense in $\mathbb T^3$. Further, we calculate push-forwards of the Lebesgue measure on a local strong unstable manifold. These numeric data indicate that the sequence of push-forwards converges to the SRB measure.

math.DS↗

arXiv2017

Stratified construction of neural network based interatomic models for multicomponent materials

Recent application of neural networks (NNs) to modeling interatomic interactions has shown the learning machines' encouragingly accurate performance for select elemental and multicomponent systems. In this study, we explore the possibility of building a library of NN-based models by introducing a hierarchical NN training. In such a stratified procedure NNs for multicomponent systems are obtained by sequential training from the bottom up: first unaries, then binaries, and so on. Advantages of constructing NN sets with shared parameters include acceleration of the training process and intact description of the constituent systems. We use an automated generation of diverse structure sets for NN training on density functional theory-level reference energies. In the test case of Cu, Pd, Ag, Cu-Pd, Cu-Ag, Pd-Ag, and Cu-Pd-Ag systems, NNs trained in the traditional and stratified fashions are found to have essentially identical accuracy for defect energies, phonon dispersions, formation energies, etc. The models' robustness is further illustrated via unconstrained evolutionary structure searches in which the NN is used for the local optimization of crystal unit cells.

cond-mat.mtrl-sci↗

arXiv2016

BaSn$_2$: A new, wide-gap, strong topological insulator

BaSn$_2$ has been shown to form as layers of buckled stanene intercalated by barium ions~\cite{Kim_2008}. However, despite an apparently straightforward synthesis and significant interest in stanene as a topological material, BaSn$_2$ has been left largely unexplored, and has only recently been recognized as a potential topological insulator. Belonging to neither the lead nor bismuth chalcogenide families, it would represent a unique manifestation of the topological insulating phase. Here we present a detailed investigation of BaSn$_2$, using both {\it ab initio} and experimental methods. First-principles calculations demonstrate that this overlooked material is a indeed strong topological insulator with a bulk band gap of 360meV, among the largest observed for topological insulators. We characterize the surface state dependence on termination chemistry, providing guidance for experimental efforts to measure and manipulate its topological properties. Additionally, through {\it ab initio} modeling and synthesis experiments we explore the stability and accessibility of this phase, revealing a complicated phase diagram that indicates a challenging path to obtaining single crystals.

cond-mat.mtrl-sci↗