arXiv subjects

Nikita Rybin

Publications and source records attributed to Nikita Rybin.

17 recordsLinked to original sources

arXiv2026

Low-rank approximation of Moment Tensor Potential enables reducing training set size without loss of accuracy

In this study, we implement a low-rank approximation of Moment Tensor Potential (MTP) based on the tensor train (TT) decomposition. The implemented tensor-factorized MTP (TFMTP) and the original MTP model are actively trained via a MaxVol-based algorithm during molecular dynamics simulations of a four-component molten salt mixture, LiF-NaF-KF (FLiNaK), and geometry optimizations of a five-component equiatomic MoNbTaWV random alloy. We demonstrate that under a 1.5-fold compression, TFMTP requires two times fewer configurations for fitting than the original MTP model, while maintaining an indistinguishable level of accuracy. These actively trained MTP and TFMTP models are further used to evaluate the density and viscosity of FLiNaK at temperatures ranging from 600 to 1200 K, as well as the elastic constants and bulk modulus of the MoNbTaWV alloy at zero temperature. For both atomic systems, the differences in physical properties predicted by MTP and TFMTP are negligible.

physics.chem-ph↗

arXiv2026

Integrating moment tensor potentials with finite-element modeling for heat transfer prediction in FLiBe-based molten salt systems

Molten fluoride salts are promising heat-transfer media for advanced molten salt reactors (MSRs), where reliable thermophysical property determination is critical for component design and safety. We present an integrated multiscale framework that couples machine-learning-driven atomistic simulations with finite-element (FE) modeling to predict the heat-transfer performance of FLiBe-based salts in a linear heat exchanger. At the atomistic scale, Moment Tensor Potentials (MTPs), actively trained on ab initio data, are developed for pure FLiBe (66-34 and 74-26 LiF-BeF2 mol%), FLiBe-LaF3, and FLiBe-UF4. These potentials are used in molecular dynamics simulations to obtain temperature- and composition-dependent transport properties (density, viscosity, thermal conductivity, and isobaric heat capacity), which are mapped as inputs to a three-dimensional FE model of the experimental thermal loop. The FE model with literature transport properties reproduces the experimental heat-transfer behavior of pure FLiBe to within 10% in the laminar regime and 18% in the transitional and turbulent regimes, validating the end-to-end pipeline for this composition. The same model with MTP-MD-derived transport properties systematically overestimates the heat-transfer coefficient by 25-28%, an offset consistent with the MTP-MD biases on thermal conductivity and viscosity. Applied to the ternary systems FLiBe-LaF3 and FLiBe-UF4 over 0-5 mol%, the MTP-MD-driven FE model predicts a mean reduction in heat-transfer efficiency of 8-11% relative to pure FLiBe, with UF4 exhibiting the strongest effect. The qualitative ordering of the three systems is the more robust result; the absolute value of the 8-11% figure is contingent on the MTP accuracy. The framework is complementary to high-temperature experiments and provides a physics-based pathway for the rapid screening of MSR coolant formulations.

cond-mat.mtrl-sci↗

arXiv2026

Thermal Conductivity and Temperature-Induced Band Gap Renormalization in Crystalline and Amorphous Ga$_2$O$_3$

The lattice thermal conductivity (LTC) and electron-phonon interactions in crystalline and amorphous gallium oxide are herein determined by coupling a machine-learned interatomic potential, namely the moment tensor potential (MTP) model, to first-principles calculations. Crystalline $\beta$-Ga$_2$O$_3$ exhibits a substantial band gap renormalization (BGR) of $\sim$0.45 eV at 700 K, with $\sim$0.2 eV caused by zero-point BGR. The computed temperature dependence of BGR induced by classical nuclear motion in $\beta$-Ga$_2$O$_3$ is stronger than that in amorphous Ga$_2$O$_3$, with the difference in BGR reaching $\sim$0.18 eV at 900 K. Thermal transport calculations reveal that the LTC of amorphous Ga$_2$O$_3$ remains near $0.9$ W$\cdot$ m$^{-1}$$\cdot$K$^{-1}$ for temperatures between 300 K and 700 K, which is approximately an order of magnitude lower than that of crystalline $\beta$-Ga$_2$O$_3$. Overall, the presented framework provides a computationally tractable and reliable route for predicting properties of semiconductors (both crystalline and amorphous) under operating conditions relevant to microelectronics and optoelectronics.

cond-mat.mtrl-sci↗

arXiv2026

Global Optimization of Atomic Clusters via Physically-Constrained Tensor Train Decomposition

The global optimization of atomic clusters represents a fundamental challenge in computational chemistry and materials science due to the exponential growth of local minima with system size (i.e., the curse of dimensionality). We introduce a novel framework that overcomes this limitation by exploiting the low-rank structure of potential energy surfaces through Tensor Train (TT) decomposition. Our approach combines two complementary TT-based strategies: the algebraic TTOpt method, which utilizes maximum volume sampling, and the probabilistic PROTES method, which employs generative sampling. A key innovation is the development of physically-constrained encoding schemes that incorporate molecular constraints directly into the discretization process. We demonstrate the efficacy of our method by identifying global minima of Lennard-Jones clusters containing up to 45 atoms. Furthermore, we establish its practical applicability to real-world systems by optimizing 20-atom carbon clusters using a machine-learned Moment Tensor Potential, achieving geometries consistent with quantum-accurate simulations. This work establishes TT-decomposition as a powerful tool for molecular structure prediction and provides a general framework adaptable to a wide range of high-dimensional optimization problems in computational material science.

math.OC↗

arXiv2025

Active learning and explicit electrostatics enable accurate modeling of electrolytes

Machine learning interatomic potentials (MLIPs) offer near-\textit{ab initio} accuracy with the efficiency of classical force fields, making them attractive for modeling electrolytes. Collecting a diverse training set is essential for their accuracy and reliability, and explicit treatment of strong electrostatic interactions may be necessary. In this work, we demonstrated that D-optimality-based active learning can automatically generate diverse training sets for moment tensor potentials (MTPs), enabling reliable molecular dynamics simulations of pure ethylene carbonate (EC), ethyl methyl carbonate (EMC), their mixtures, and LiPF$_6$ solutions. The resulting MTPs exhibit excellent transferability across various EC/EMC compositions, producing ionic conductivities within 11\% mean deviations from experiments. In addition, we assessed the impact of explicitly incorporating electrostatics by augmenting MTP with charge redistribution schemes using either fixed or environment-dependent charges. Our results show that the augmented MTP achieves the same or higher accuracy than standard model with fewer parameters, while environment-dependent charges further improve accuracy and the stability of simulations.

physics.chem-ph↗

arXiv2025

Incorporating Coulomb interactions with fixed charges in Moment Tensor Potentials and Equivariant Tensor Network Potentials

In this work, we incorporate long-range electrostatic interactions in the form of the Coulomb model with fixed charges into the functional form of short-range machine-learning interatomic potentials (MLIPs), particularly in the Moment Tensor Potential and Equivariant Tensor Network potential. We show that explicit incorporation of the Coulomb interactions with fixed charges leads to a significant reduction of energy fitting errors, namely, more than four times, of short-range MLIPs trained on organic dimers of charged molecules. Furthermore, with our long-range models we demonstrate a significant improvement in the prediction of the binding curves of the organic dimers of charged molecules. Finally, we show that the results calculated with MLIPs are in good correspondence with those obtained with density functional theory for organic dimers of charged molecules.

physics.chem-ph↗

arXiv2025

Low-rank matrix and tensor approximations for compression of machine-learning interatomic potentials

Machine-learning interatomic potentials (MLIPs) have become a mainstay in computationally-guided materials science, surpassing traditional force fields due to their flexible functional form and superior accuracy in reproducing physical properties of materials. This flexibility is achieved through mathematically-rigorous basis sets that describe interatomic interactions within a local atomic environment. The number of parameters in these basis sets influences both the size of the training dataset required and the computational speed of the MLIP. Consequently, compressing MLIPs by reducing the number of parameters is a promising route to more efficient simulations. In this work, we use low-rank matrix and tensor factorizations under fixed-rank constraints to achieve this compression. In addition, we demonstrate that an algorithm with automatic rank augmentation helps to find a deeper local minimum of the fitted potential. The methodology is mainly verified using the Moment Tensor Potential (MTP) model and benchmarked on multi-component systems: a Mo-Nb-Ta-W medium-entropy alloy, molten LiF-NaF-KF, and a glycine molecular crystal. The proposed approach achieves up to 50 % compression without any loss of MTP accuracy. We also demonstrate that the developed methodology is universal and can be applied to compress other MLIPs on the example of Atomic Cluster Expansion (ACE).

physics.chem-ph↗

arXiv2025

Efficient Band Structure Unfolding with Atom-centered Orbitals: General Theory and Application

Band structure unfolding is a key technique for analyzing and simplifying the electronic band structure of large, internally distorted supercells that break the primitive cell's translational symmetry. In this work, we present an efficient band unfolding method for atomic orbital (AO) basis sets that explicitly accounts for both the non-orthogonality of atomic orbitals and their atom-centered nature. Unlike existing approaches that typically rely on a plane-wave representation of the (semi-)valence states, we here derive analytical expressions that recasts the primitive cell translational operator and the associated Bloch-functions in the supercell AO basis. In turn, this enables the accurate and efficient unfolding of conduction, valence, and core states in all-electron codes, as demonstrated by our implementation in the all-electron ab initio simulation package FHI-aims, which employs numeric atom-centered orbitals. We explicitly demonstrate the capability of running large-scale unfolding calculations for systems with thousands of atoms and showcase the importance of this technique for computing temperature-dependent spectral functions in strongly anharmonic materials using CuI as example.

cond-mat.mtrl-sci↗

arXiv2025

Tuning Thermal Conductivity and Electron-Phonon Interactions in Carbon and Boron Nitride Moir\'e Diamanes via Twist Angle Manipulation

We have investigated the effect of interlayer twist angle on lattice thermal conductivity (LTC) and band gap renormalization in boron nitride and carbon Moir\'e diamanes. Moment tensor potentials were used for calculating energies and forces of interatomic interactions. The methods based on the solution of Boltzmann transport equation (BTE) for phonons and the GreenKubo (GK) formula were utilized to calculate LTC. The 20-40 % difference in LTC values obtained with GK and BTE-based methods showed the importance of high-order anharmonic contributions to LTC. Significant reduction (by 4.5 - 9 times) of the in-plane LTC with the twist angle increase caused by the growth of structural disorder was observed in the Moir\'e diamanes. This growth of disorder also leads to higher band gap renormalization (induced by classical nuclei motion) in the structures with higher twist angles. Significant band gap renormalization values obtained considering the quantum nuclear effects are caused by the high phonon frequencies related to the bonds with hydrogen atoms on the Moir\'e diamanes surfaces. Understanding of the twist angle effect on LTC and electron-phonon coupling in the Moir\'e diamanes provides a fundamental basis for manipulating their thermal and electronic properties, making these materials promising for thermoelectrics, microelectronics and optoelectronics.

cond-mat.mtrl-sci↗

arXiv2025

Moment Tensor Potential and Equivariant Tensor Network Potential with explicit dispersion interactions

In this study, we investigate the effect of incorporating explicit dispersion interactions in the functional form of machine learning interatomic potentials (MLIPs), particularly in the Moment Tensor Potential and Equivariant Tensor Network potential for accurate modeling of liquid carbon tetrachloride, methane, and toluene. We show that explicit incorporation of dispersion interactions via D2 and D3 corrections significantly improves the accuracy of MLIPs when the cutoff radius is set to a commonly used value of 5 -- 6 \r{A}. We also show that for carbon tetrachloride and methane, a substantial improvement in accuracy can be achieved by extending the cutoff radius to 7.5 \r{A}. However, for accurate modeling of toluene, explicit incorporation of dispersion remains important. Furthermore, we find that MLIPs incorporating dispersion interactions via D2 reach a close level of accuracy to those incorporating D3, and D2 is suitable for accurate modeling of the systems in the study, while being less computationally expensive. We evaluated the accuracy of MLIPs in dimer binding curves compared to ab initio data and in predicting density and radial distribution functions compared to experiments.

physics.chem-ph↗

arXiv2025

Novel Strontium Carbides Under Compression

Exploring the chemistry of materials at high pressures has lead to the discovery of previously unknown exotic compounds. Here, we systematically search for all thermodynamically stable Sr-C compounds under pressure (up to 100 GPa) using the ab initio evolutionary crystal structure prediction method. Our search lead to the discovery of hitherto unknown phases of SrC3, Sr2C5, Sr2C3, Sr2C, Sr3C2, and SrC. The newly discovered crystal structures feature a variety of different carbon environments ranging from isolated C anions and C-dimers to exotic polyatomic carbon anions including chains, stripes, and infinite ribbons consisting of pentagonal C5 and hexagonal C6 rings. Dynamical stability of all predicted compounds is confirmed by phonons calculations. Bader analysis unravels very diverse chemistry in these compounds and bonding patterns in some of them can be described using Zintl-Klemm rule.

cond-mat.mtrl-sci↗

arXiv2025

Polarisation, Born Effective Charges, and Topological Invariants via a Berry-Phase Approach

This paper represents one contribution to a larger Roadmap article reviewing the current status of the FHI-aims code. In this contribution, the implementation of polarization, Born-effective charges and topological invariants using a Berry-phase approach in a all-electron, numeric atom-centered orbitals framework is summarized. Guidelines on usage and links to tutorials are provided.

cond-mat.mtrl-sci↗

arXiv2024

Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning

We present an algorithm for accelerating the search of molecule's adsorption site based on global optimization of surface adsorbate geometries. Our approach uses a machine-learning interatomic potential (moment tensor potential) to approximate the potential energy surface and an active learning algorithm for the automatic construction of an optimal training dataset. To validate our methodology, we compare the results across various well-known catalytic systems with surfaces of different crystallographic orientations and adsorbate geometries, including CO/Pd(111), NO/Pd(100), NH$_3$/Cu(100), C$_6$H$_6$/Ag(111), and CH$_2$CO/Rh(211). In the all cases, we observed an agreement of our results with the literature.

cond-mat.mtrl-sci↗

arXiv2024

Temperature-dependent Electronic Spectral Functions from Band-Structure Unfolding

The electronic band structure, describing the periodic dependence of electronic quantum states on lattice momentum in reciprocal space, is a fundamental concept in solid-state physics. However, it's only well-defined for static nuclei. To account for thermodynamic effects, this concept must be generalized by introducing the temperature-dependent spectral function, which characterizes the finite-width distributions of electronic quantum states at each reciprocal vector. Many-body perturbation theory can compute spectral functions and associated observables, but it approximates the dynamics of nuclei and its coupling to the electrons using the harmonic approximation and linear-order electron-phonon coupling elements, respectively. These approximations may fail at elevated temperatures or for mobile atoms. To avoid inaccuracies, the electronic spectral function can be obtained non-perturbatively, capturing higher-order couplings between electrons and vibrational degrees of freedom. This process involves recovering the representation of supercell bands in the first Brillouin zone of the primitive cell, a process known as unfolding. In this contribution, we describe the implementation of the band-structure unfolding technique in the electronic-structure theory package FHI-aims and the updates made since its original development.

cond-mat.mtrl-sci↗

arXiv2024

Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential

Inspired by the recent success of machine-learned interatomic potentials for crystal structure prediction of the inorganic crystals, we present a methodology that exploits Moment Tensor Potentials and active learning (based on maxvol algorithm) to accelerate structure prediction of molecular crystals. Benzene and glycine are used as test systems. Interestingly, among obtained low energy structures of benzene we have found a peculiar polymeric benzene structure.

cond-mat.mtrl-sci↗

arXiv2024

A moment tensor potential for lattice thermal conductivity calculations of alpha and beta phases of Ga2O3

Calculations of heat transport in crystalline materials have recently become mainstream, thanks to machine-learned interatomic potentials that allow for significant computational cost reductions while maintaining the accuracy of first-principles calculations. Moment tensor potentials (MTP) are among the most efficient and accurate models in this regard. In this study, we demonstrate the application of MTP to the calculation of the lattice thermal conductivity of alpha and beta Ga2O3. Although MTP is commonly employed for lattice thermal conductivity calculations, the advantages of applying the active learning methodology for potential generation is often overlooked. Here, we emphasize its importance and illustrate how it enables the generation of a robust and accurate interatomic potential while maintaining a moderate-sized training dataset.

cond-mat.mtrl-sci↗

arXiv2020

Novel Copper Fluoride Analogs of Cuprates

On the basis of the first-principles evolutionary crystal structure prediction of stable compounds in the Cu-F system, we predict two experimentally unknown stable phases -- Cu2F5 and CuF3. Cu2F5 comprises two interacting magnetic subsystems with the Cu atoms in the oxidation states +2 and +3. CuF3 contains magnetic Cu 3+ ions forming a lattice with the antiferromagnetic coupling. We showed that some or all of Cu 3+ ions can be reduced to Cu 2+ by electron doping, as in the well known KCuF3. Significant similarities between the electronic structures calculated in the framework of DFT+U suggest that doped CuF3 and Cu2F5 may exhibit high-temperature superconductivity with the same mechanism as in cuprates.

cond-mat.mtrl-sci↗