arXiv subjects

Alexander Shapeev

Publications and source records attributed to Alexander Shapeev.

At least 19 recordsLinked to original sources

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

HEDGEHOG: Hierarchical Evaluation of Drug Generators Through Rigorous Filtration

Generative molecular models can support early drug discovery by proposing new candidate compounds de novo. In practice, useful candidates must balance target-relevant activity, synthetic accessibility, physicochemical properties, and other multiparameter design constraints. However, metrics commonly used to evaluate molecular generators only weakly reflect whether the generated compounds are medicinally plausible and suitable for downstream computation. This can produce false positives in model evaluation, incorrect assumptions, and inefficient use of computational resources. We introduce HEDGEHOG, a unified six-stage filtration benchmark that is inspired by industrial hit identification workflows: (i) preprocessing; (ii) physicochemical descriptor screening; (iii) structural alerts and graph-sanity checks; (iv) synthesis feasibility; (v) docking and binding affinity estimation; and (vi) three-dimensional pose and interaction checks. We evaluate 23 molecular generators across three model classes under a standardized protocol. Across 230,000 generated molecules, only 0.65% of initial molecules survive all stages. Our results expose a central limitation of current molecular generators: molecules that appear acceptable under isolated criteria rarely satisfy medicinal chemistry, synthesis, docking, and 3D pose filters simultaneously.

cs.LG↗

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

Automated Prediction of Thermodynamic Properties via Bayesian Free-Energy Reconstruction from Molecular Dynamics

Accurate free-energy calculations are essential for predicting thermodynamic properties and phase stability, but existing methods are limited: phonon-based approaches neglect anharmonicity and liquids, while molecular dynamics (MD) is computationally demanding, neglects low-temperature quantum effects, and often requires manual planning and post-processing of simulations. We present a unified workflow that reconstructs the Helmholtz free-energy surface from MD data using Gaussian Process Regression (GPR), augmented with zero-point energy corrections from harmonic/quasi-harmonic theory. The framework propagates statistical uncertainties, mitigates finite-size effects, and employs active learning to optimize sampling in the volume-temperature space. It applies seamlessly to both crystalline and liquid phases. We demonstrate the methodology by computing heat capacities, thermal expansion, isothermal and adiabatic bulk moduli, and melting properties for nine elemental FCC and BCC metals using 20 classical and machine-learned interatomic potentials, with all predictions accompanied by quantified confidence intervals. Automated, general, and uncertainty-aware, the workflow advances high-throughput thermodynamics and provides a systematic benchmark for interatomic potentials.

cond-mat.mtrl-sci↗

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

Clarifying the Ti-V Phase Diagram Using First-Principles Calculations and Bayesian Learning

Conflicting experiments disagree on whether the titanium-vanadium (Ti-V) binary alloy exhibits a body-centred cubic (BCC) miscibility gap or remains completely soluble. A leading hypothesis attributes the miscibility gap to oxygen contamination during alloy preparation. To resolve this disagreement, we use an ab initio + machine-learning workflow that couples an actively-trained Moment Tensor Potential with Bayesian inference of free energy surface. This workflow enables construction of the Ti-V phase diagram across the full composition range with systematically reduced statistical and finite-size errors. The resulting diagram reproduces all experimental features, demonstrating the robustness of our approach, and clearly favors the variant with a BCC miscibility gap terminating at T = 980 K and c = 0.67. Because our simulations model a perfectly oxygen-free Ti-V system, the observed gap cannot originate from impurity effects, in contrast to recent CALPHAD reassessments.

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

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↗

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

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↗

arXiv2024

Thermophysical Properties of Molten FLiNaK: a Moment Tensor Potential Approach

Fluoride salts demonstrate significant potential for applications in next-generation nuclear reactors, necessitating a comprehensive understanding of their thermophysical properties for technological advancements. Experimental measurement of these properties poses challenges, due to factors such as high temperatures, impurity control, and corrosion. Consequently, precise computational modeling methods become essential for predicting the thermophysical properties of molten salts. In this work, we performed molecular dynamics (MD) simulations of several thermophysical properties of the eutectic salt mixture LiF-NaF-KF (FLiNaK) melt, including density, self-diffusion coefficients, viscosity, and thermal conductivity. We demonstrated the successful application of moment tensor potentials (MTP) as an accurate model for interatomic interactions in FLiNaK. Our results on thermophysical properties calculations exhibit strong agreement with experimental data. An important aspect of our methodology is the incorporation of an active learning scheme, which enables the generation of a robust and accurate potential, while maintaining a moderate-sized training dataset.

cond-mat.mtrl-sci↗

arXiv2023

Bayesian inference of composition-dependent phase diagrams

Phase diagrams serve as a highly informative tool for materials design, encapsulating information about the phases that a material can manifest under specific conditions. In this work, we develop a method in which Bayesian inference is employed to combine thermodynamic data from molecular dynamics (MD), melting point simulations, and phonon calculations, process these data, and yield a temperature-concentration phase diagram. The employed Bayesian framework yields not only the free energies of different phases as functions of temperature and concentration but also the uncertainties of these free energies originating from statistical errors inherent to finite-length MD trajectories. Furthermore, it extrapolates the results of the finite-atom calculations to the infinite-atom limit and facilitates the choice of temperature, chemical potentials, and the number of atoms conducting the next simulation with which will be the most efficient in reducing the uncertainty of the phase diagram. The developed algorithm was successfully tested on two binary systems, Ge-Si and K-Na, in the full range of concentrations and temperatures.

cond-mat.mtrl-sci↗

arXiv2023

Accurate melting point prediction through autonomous physics-informed learning

We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. Given the interatomic interaction model, the method makes decisions regarding the number of atoms and temperature at which to conduct simulations, and based on the collected data predicts the melting point along with the uncertainty, which can be systematically improved with more data. We demonstrate how incorporating physical models of the solid-liquid coexistence evolution enhances the algorithm's accuracy and enables optimal decision-making to effectively reduce predictive uncertainty. To validate our approach, we compare the results of 20 melting point calculations from the literature to the results of our calculations, all conducted with same interatomic potentials. Remarkably, we observe significant deviations in about one-third of the cases, underscoring the need for accurate and reliable algorithms for materials property calculations.

cond-mat.mtrl-sci↗

arXiv2023

MLIP-3: Active learning on atomic environments with Moment Tensor Potentials

Nowadays, academic research relies not only on sharing with the academic community the scientific results obtained by research groups while studying certain phenomena, but also on sharing computer codes developed within the community. In the field of atomistic modeling these were software packages for classical atomistic modeling, later -- quantum-mechanical modeling, and now with the fast growth of the field of machine-learning potentials, the packages implementing such potentials. In this paper we present the MLIP-3 package for constructing moment tensor potentials and performing their active training. This package builds on the MLIP-2 package (Novikov et al. (2020), The MLIP package: moment tensor potentials with MPI and active learning. Machine Learning: Science and Technology, 2(2), 025002.), however with a number of improvements, including active learning on atomic neighborhoods of a possibly large atomistic simulation.

physics.comp-ph↗

arXiv2023

Equivariant Tensor Network Potentials

Machine-learning interatomic potentials (MLIPs) have made a significant contribution to the recent progress in the fields of computational materials and chemistry due to the MLIPs' ability of accurately approximating energy landscapes of quantum-mechanical models while being orders of magnitude more computationally efficient. However, the computational cost and number of parameters of many state-of-the-art MLIPs increases exponentially with the number of atomic features. Tensor (non-neural) networks, based on low-rank representations of high-dimensional tensors, have been a way to reduce the number of parameters in approximating multidimensional functions, however, it is often not easy to encode the model symmetries into them. In this work we develop a formalism for rank-efficient equivariant tensor networks (ETNs), i.e. tensor networks that remain invariant under actions of SO(3) upon contraction. All the key algorithms of tensor networks like orthogonalization of cores and DMRG-based algorithms carry over to our equivariant case. Moreover, we show that many elements of modern neural network architectures like message passing, pulling, or attention mechanisms, can in some form be implemented into the ETNs. Based on ETNs, we develop a new class of polynomial-based MLIPs that demonstrate superior performance over existing MLIPs for multicomponent systems.

physics.comp-ph↗

arXiv2022

AI-accelerated Materials Informatics Method for the Discovery of Ductile Alloys

In computational materials science, a common means for predicting macroscopic (e.g., mechanical) properties of an alloy is to define a model using combinations of descriptors that depend on some material properties (elastic constants, misfit volumes, etc.), representative for the macroscopic behavior. The material properties are usually computed using special quasi-random structures (SQSs), in tandem with density functional theory (DFT). However, DFT scales cubically with the number of atoms and is thus impractical for a screening over many alloy compositions. Here, we present a novel methodology which combines modeling approaches and machine-learning interatomic potentials. Machine-learning interatomic potentials are orders of magnitude faster than DFT, while achieving similar accuracy, allowing for a predictive and tractable high-throughput screening over the whole alloy space. The proposed methodology is illustrated by predicting the room temperature ductility of the medium-entropy alloy Mo-Nb-Ta.

cond-mat.mtrl-sci↗