Software and Projects Using KIM

OpenKIM’s primary objectives are to advance materials research by serving as a trusted, curated source of interatomic potentials and by enabling automated, high-throughput computational workflows. Its central role is evidenced by the widespread adoption of the KIM API across a diverse ecosystem of atomistic simulation codes and tools, extensive recognition in review literature, and broad use of OpenKIM potentials in scientific applications.

Below is a summary of OpenKIM usage illustrating its impact (last updated March 25, 2026).

Atomistic and multiscale simulation codes

  • - As Soon As Possible (ASAP) is a calculator for doing large-scale classical molecular dynamics within the Atomic Simulation Environment (ASE). ASAP has full support for KIM Portable Models (KIM API v2) as of version 3.11.3. The ASAP manual can be found here and installation instructions here.

  • – The Atomic Simulation Environment (ASE) is a set of tools and Python modules for setting up, manipulating, running, visualizing and analyzing atomistic simulations. ASE supports various simulators using "calculators" that provide an interface to these codes. A KIM calculator for running Portable Models and Simulator Models (for simulators that have an ASE calculator) is available in ASE as of version 3.19.0. See here for instructions and examples.

  • – DL_POLY is a general purpose classical molecular dynamics simulation software developed at Daresbury Laboratory in the UK. DL_POLY has support for KIM API v2 (released Sep 2000). For more information on DL_POLY and to obtain the code, visit the DL_POLY website.

  • – exaStamp is a high-performance molecular dynamics simulation package built on the onika/exaNBody framework. Its primary focus is microscopic-scale material modeling under extreme conditions, with particular strengths in high strain rates, shock physics, and small-scale mechanic. exaStamp has support for KIM API v2, allowing seamless access to the extensive library of KIM-compliant interatomic potentials. General guidelines for building exaStamp can be found in the related documentation with additional instructions to get KIM support.

  • – GULP is a molecular simulation program emphasizing analytical solutions based on lattice dynamics, but also including a molecular dynamics mode. GULP provides full support for KIM Portable Models as of version 4.2. To use KIM with GULP you must add the flag -DKIM to DEFS in getmachine so that the code that supports KIM is enabled during compilation. Models are then specified using the kim\_model option. At present only a single type of each element is supported when using KIM and so species types are ignored when being passed to KIM models.

  • – ITAP Molecular Dynamics (IMD) is a software package for classical molecular dynamics simulations, originally developed by the late Jörg Stadler within the framework of Project C14 at the Institute for Theoretical and Applied Physics (ITAP) at the University of Stuttgart. Johannes Roth, alongside other members of the ITAP group, has been involved in the ongoing development and application of this software, which was primarily used for studies in condensed matter physics.

  • – The molecular dynamics program LAMMPS has full support for the KIM API v2 as of 28 Feb 2019. This support is implemented in the KIM package in LAMMPS. See the general instructions for building LAMMPS and specific instructions for building LAMMPS with KIM support. To run LAMMPS with a KIM interatomic potential, install it (see instructions on how to do this on the Obtaining KIM Models page). Then follow the instructions in the LAMMPS documentation on how to use KIM Models in LAMMPS.

  • – QUIP (QUantum mechanics and Interatomic Potentials is a collection of software tools to carry out molecular dynamics simulations. It implements a variety of interatomic potentials and includes support for OpenKIM models, as well as tight binding quantum mechanics, and is also able to call external packages, and serve as plugins to various software platforms.

  • – Molly.jl is an open-source Julia package designed for molecular dynamics (MD) and physical system simulations, written entirely in Julia. It enables simulating atomic interactions with a focus on differentiable molecular simulation (allowing automatic differentiation), GPU acceleration (CUDA), and customizable interactions, supporting research in proteins and molecular systems. KIM usage is described in the documentation.

  • – The quasicontinuum (QC) method is a multiscale simulation platform in which fully-resolved atomistic regions are embedded in a coarse-grained finite element regions. A high-performance 3D version of the QC code has been developed with support for polycrystalline materials of arbitrary crystal structure (simple and complex lattices). The QC code provides full support for KIM API v2. For more information on the code and how to access it, contact the developers.

Workflow tools

  • – Conda packages are available for easy installation of various OpenKIM packages including: The KIM API (kim-api), automated convergence detection for computational protocols (kim-convergence), query interface to openkim.org (kim-query), and the library of OpenKIM potentials (openkim-models).

  • – EasyBuild packages are available for easy installation of the KIM API (kim-api) and the library of OpenKIM potentials (openkim-models).

  • – The iprPy package is a computational framework supporting open source calculation methods. The framework focuses on making the barriers for usage as low as possible for both users and developers of calculations. The primary focus is on classical atomistic calculations using interatomic potentials.

  • – Pyiron is an integrated development environment (IDE) for computational materials science focused on simplifying the development of simulation protocols. It enables users to quickly iterate over all KIM potentials to test and validate their simulation protocols, by leveraging the LAMMPS KIM interface. (See here for examples of using pyiron with KIM.)

  • – Orchestrator is general-purpose computational framework developed at LLNL for streamlining the complex workflows of building, training, and analyzing interatomic potentials alongside the execution and analysis of large scale MD simulations to answer fundamental scientific questions. Orchestrator is built upon OpenKIM infrastrcture including the KIMkit Python package providing standlone hosting of interatomic potentials, and the KIM Developer Platform (KDP). For more information on KIM usage, see the documentation.

  • – The Simulation Environment for Atomistic and Molecular Modeling (SEAMM) is a user-friendly environment for computational molecular and materials science. SEAMM is a workflow manager composed of plug-ins that wrap popular software packages and tools allowing a user to set up and run quantum and classical molecular simulations. SEAMM is developed by the Molecular Sciences Software Institute (MolSSI).

Atomistic analysis tools and projects

  • – Carbon Potentials is an interactive online tool for comparing carbon interatomic potentials. The tool is designed to determine the transferability of the many carbon potentials available in the literature. Each potential is used to simulate various properties of carbon and the results are presented in this tool.

  • – The ColabFit Exchange is an open-access online database for discovering, exploring, and contributing datasets aimed at developing data-driven interatomic potentials (DDIPs) for materials science and chemistry. ColabFit aims to create a computational framework that enables researchers to rapidly develop and deploy DDIPs for complex material systems by connecting existing cyberinfrastructure resources of first principles and experimental data with a variety of fitting frameworks, and to share developed DDIPs through OpenKIM.

  • – Enalos Cloud Platform developed by NovaMechanics Ltd, is an online, freely available cheminformatics and nanoinformatics cloud platform. It hosts a broad range of predictive models provided as web services, offering flexible and powerful cloud computing resources that reduce the barriers to entry for complex scientific calculations. Enalos Cloud tools using OpenKIM interatomic potentials include:
    • ASCOT is a Web tool for the digital reconstruction of energy minimized Ag, CuO, TiO2 spherical nanoparticles and calculation of their atomistic descriptors.
    • NanoConstruct is a nanoparticle construction tool.
    • NanotubeConstruct is a nanotube construction tools.

  • – As part of the EU SSbD4CheM project, the HydroNanoConstruct tool was deployed on the EosCloud web platform. It is a nanoparticle construction tool for hydrated metal oxides utilizing OpenKIM interatomic potentials.

  • – Joint Automated Repository for Various Integrated Simulations (JARVIS) leaderboard for evaluating force fields includes OpenKIM potentials.

  • – MDStressLab++ is a package for post-processing molecular dynamics or molecular statics results to obtain stress fields using different definitions of the atomistic stress tensor. MDStressLab++ currently supports KIM API v2. Both Fortran and C++ versions are available. For more information, contact the developers.

  • – nanoHUB is an open-access platform providing online simulation tools, educational resources, and data for nanotechnology, engineering, and data science. NanoHUB tools using OpenKIM interatomic potentials include:
    • LAMMPS is a NanoHub tool to run LAMMPS by uploading a data file and command script.
    • LLM for LAMMPS used OpenAI's GPT-4 to manage LAMMPS simulations including generating input scripts, running simulations, and querying the results via the thermodynamic output.
    • Minimal Molecular Simulation - Perform simple molecular dynamics and statics simulations.
    • Nanomaterials Mechanics Explorer enables users to explore properties of materials from the atomistic scale including dislocation motion, crack propagation, plastic deformation, melting, and martensitic transformation.
    • SimTool is a NanoHub tool for performing findable, accessible, interoperable, and reusable (FAIR) simulations utilizing OpenKIM interatomic potentials. This application is based on the Sim2Ls framework.

  • – The Virtual Fab simulation laboratory provides an interactive platform to construct, carry out, and analyze simulations pertaining to nanoscale devices, with an emphasis on semiconductors. Virtual Fab currently offers full support for the use of OpenKIM Models along with a visualization interface available for plotting and comparing KIM Test Results for Models relevant to a given application.

  • VSSR-MC – Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) is an algorithm for sampling surface reconstructions. VSSR-MC samples across both compositional and configurational spaces.

  • – Atomistic Machine-learning Package (AMP) is open-source package designed to easily bring machine-learning to atomistic calculations. Use of OpenKIM interatomic potentials is described in the documentation.

  • – The KIM-based learning-integrated fitting framework (KLIFF) is a package for fitting analytic and machine learning interatomic potentials (IPs). Trained IPs are compatible with the KIM API and can be used with other codes that are compatible with KIM (such as those listed on this page). KLIFF is written in Python with computationally intensive components implemented in C++. The code is modular by design enabling a flexible approach to fitting incorporating different components: atomic environment descriptors, functional forms, loss functions, optimizers, and quality analyzers. The package is available through GitHub with extensive documentation and examples available here. An article describing the KLIFF packages is available here.

  • PANNA – Properties from Artificial Neural Network Architectures (PANNA) is a package to train and validate machine learned interatomic potentials based on different atomic environement descriptors and atomic multilayer perceptrons. PANNA parameterizations can be deployed via the OpenKIM PANNA Model Driver.

  • – Potfit is a free implementation of the force-matching algorithm to generate effective potentials from ab-initio reference data. Potfit provides support for KIM API v2 as of version 20190325.

  • PolyMLP – pypolymlp is Python code for generating Polynomial Machine Leaning Potentials (PolyMLP) based on datasets generated from density functional theory (DFT) calculations. In addition to potential development, pypolymlp allows users to compute various physical properties and perform atomistic simulations using the trained MLPs. PolyMLP parameterizations can be deployed via the OpenKIM PolyMLP Model Driver.

Review papers

This is a partial list of review papers that laud OpenKIM's role in open, reproducible computational science and materials informatics. If a paper is missing from this list, let us know by contacting us at support@openkim.org.

  • M. Salas, A. Singh, C. Pignataro, L. Pal, "AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing", Communications Materials, 7, 1 (2026). doi:10.1038/s43246-026-01105-0

  • S. Joshi, A. Bucsek, D. C. Pagan, S. Daly, S. Ravindran, J. Marian, M. A. Bessa, S. R. Kalidindi, N. C. Admal, C. Reina, S. Ghosh, J. A. Warren, J. Viñals, E. B. Tadmor, "Integrated experiment and simulation co-design: A key infrastructure for predictive mesoscale materials modeling", Mechanics of Materials, 211, 105480 (2025). doi:10.1016/j.mechmat.2025.105480

  • Y. Xian, C. Li, Y. Xu, Y. Zhou, D. Xue, "AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management", SusMat, 5, 5 (2025). doi:doi.org/10.1002/sus2.70030

  • S. Xu, J. Wu, Y. Guo, Q. Zhang, X. Zhong, J. Li, W. Ren, "Applications of machine learning in surfaces and interfaces", Chemical Physics Reviews, 6, 1 (2025). doi:doi.org/10.1063/5.0244175

  • J. W. Yoon, B. Zhou, J. Senthilnath, "SG-NNP: Species-separated Gaussian Neural Network Potential with Linear Elemental Scaling and Optimized Dimensions for Multi-component Materials", arXiv (2024). doi:10.48550/arXiv.2407.06615

  • J. Akhtar, T. Kalita, B. R. Thakuria, M. J. Sarmah, H. P. Goswami, "Deep Diving into AI-Enhanced Innovative Approaches in Designing Inorganic Nanomaterials", in Multifunctional Inorganic Nanomaterials for Energy Applications, edtied by H.P. Nagaswarupa, Mika E.T. Sillanpää, H.C. Ananda Murthy, Ramachandra Naik, pp. 382-394. Boca Raton:CRC Press, 2024. doi:10.1201/9781003479239-26

  • P. Brault, "Practical classical molecular dynamics simulations for low-temperature plasma processing: a review", Reviews of Modern Plasma Physics, 8, 1 (2024). doi:10.1007/s41614-023-00140-5

  • A. Walsh, "Open computational materials science", Nature Materials, 23, 1, 16-17 (2024). doi:10.1038/s41563-023-01699-7

  • L. C. Brinson, L. M. Bartolo, B. Blaiszik, D. Elbert, I. Foster, A. Strachan, P. W. Voorhees, "Community action on FAIR data will fuel a revolution in materials research", MRS Bulletin, 49, 1, 12-16 (2024). doi:10.1557/s43577-023-00498-4

  • D. Duffy, "TYC Materials Modelling Course: Interatomic Potentials", Thomas Young Centre (2023). link

  • L. M. Ghiringhelli, C. Baldauf, T. Bereau, S. Brockhauser, C. Carbogno, J. Chamanara, S. Cozzini, S. Curtarolo, C. Draxl, S. Dwaraknath, Á. Fekete, J. Kermode, C. T. Koch, M. Kühbach, A. N. Ladines, P. Lambrix, M. Himmer, S. V. Levchenko, M. Oliveira, A. Michalchuk, R. E. Miller, B. Onat, P. Pavone, G. Pizzi, B. Regler, G. Rignanese, J. Schaarschmidt, M. Scheidgen, A. Schneidewind, T. Sheveleva, C. Su, D. Usvyat, O. Valsson, C. Wöll, M. Scheffler, "Shared metadata for data-centric materials science", Scientific Data, 10, 1 (2023). doi:10.1038/s41597-023-02501-8

  • R. X. Yang, C. A. McCandler, O. Andriuc, M. Siron, R. Woods-Robinson, M. K. Horton, K. A. Persson, "Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis", ACS Nano, 16, 19873-19891 (2022).

  • E. M. Campo, S. Shankar, A. S. Szalay, R. J. Hanisch, "Now Is the Time to Build a National Data Ecosystem for Materials Science and Chemistry Research Data", ACS Omega, 7, 16, 13398-13402 (2022). doi:10.1021/acsomega.2c00905

  • M. W. Thompson, J. B. Gilmer, R. A. Matsumoto, C. D. Quach, P. Shamaprasad, A. H. Yang, C. R. Iacovella, C. McCabe, P. T. Cummings, "Towards molecular simulations that are transparent, reproducible, usable by others, and extensible (TRUE)", Molecular Physics, 118, 9-10, e1742938 (2020). doi:10.1080/00268976.2020.1742938

  • G. J. A. Sevink, J. A. Liwo, P. Asinari, D. MacKernan, G. Milano, I. Pagonabarraga, "Unfolding the prospects of computational (bio)materials modeling", The Journal of Chemical Physics, 153, 10 (2020). doi:10.1063/5.0019773

  • National Academies of Sciences, Engineering, and Medicine. 2019. Frontiers of Materials Research: A Decadal Survey. Washington, DC: The National Academies Press. doi:10.17226/25244

  • S. Ramakrishna, T. Zhang, W. Lu, Q. Qian, J. S. C. Low, J. H. R. Yune, D. Z. L. Tan, S. Bressan, S. Sanvito, S. R. Kalidindi, "Materials informatics", Journal of Intelligent Manufacturing, 30, 6, 2307-2326 (2019). doi:10.1007/s10845-018-1392-0

  • W. Gerberich, E. B. Tadmor, J. Kysar, J. A. Zimmerman, A. M. Minor, I. Szlufarska, J. Amodeo, B. Devincre, E. Hintsala, R. Ballarini, "Review Article: Case studies in future trends of computational and experimental nanomechanics", Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, 35, 6 (2017). doi:10.1116/1.5003378

  • A. Jain, K. A. Persson, G. Ceder, "Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases", APL Materials, 4, 5 (2016). doi:10.1063/1.4944683

  • S. B. Sinnott, S. R. Phillpot, "Role of cyberinfrastructure in educating the next generation of computational materials scientists", Integrating Materials and Manufacturing Innovation, 3, 1, 85-89 (2014). doi:10.1186/2193-9772-3-7

Scientific papers

This is a partial list of papers in which OpenKIM functionality was used in scientific applications. If a paper is missing from this list, let us know by contacting us at support@openkim.org.

  • A. Tai, J. Ogbebor, R. Freitas, "Stable Machine Learning Potentials for Liquid Metals via Dataset Engineering", arXiv (2026). doi:10.48550/arXiv.2601.05003

  • S. A. Policastro, R. M. Anderson, E. A. Shockley, J. A. Keith, "Work Function Signatures of Oxide and Sulfide Formation on Cu–Ni Alloy Surfaces", The Journal of Physical Chemistry C, 130, 8, 3191-3200 (2026). doi:10.1021/acs.jpcc.5c08561

  • P. D. Kolokathis, A. Sourpis, D. Mintis, A. Tsoumanis, G. Melagraki, M. Velimirovic, I. Lynch, A. Afantitis, "HydroNanoConstruct: A Web Application for Digital Construction, Crystal Growth Investigation, and Atomistic Descriptor Calculation of Hydrated Metal Oxide Nanoparticles Powered by the EosCloud Platform", Journal of Chemical Information and Modeling, 66, 1, 1-6 (2026). doi:10.1021/acs.jcim.5c01889

  • F. Shuang, K. Liu, Y. Ji, W. Gao, L. Laurenti, P. Dey, "Modeling extensive defects in metals through classical potential-guided sampling and automated configuration reconstruction", npj Computational Materials, 11, 1 (2025). doi:10.1038/s41524-025-01599-1

  • Y. Choi, T. Brink, "Faceting transition in aluminum as a grain boundary phase transition", Physical Review Materials, 9, 8 (2025). doi:10.1103/2dnf-zdz8

  • M. Ataei, M. Modarresi, M. R. Roknabadi, A. Mogulkoc, "Molecular dynamics study of corrugation in low-defect graphene using machine learning potential", Physica Scripta, 100, 8, 086012 (2025). doi:10.1088/1402-4896/adf897

  • S. Burlison, M. F. Becker, D. Kovar, "A molecular dynamics study of high velocity impact of zinc oxide aggregates", Journal of Aerosol Science, 187, 106582 (2025). doi:10.1016/j.jaerosci.2025.106582

  • Y. Couzinié, Y. Seki, Y. Nishiya, H. Nishi, T. Kosugi, S. Tanaka, Y. Matsushita, "Machine Learning Supported Annealing for Prediction of Grand Canonical Crystal Structures", Journal of the Physical Society of Japan, 94, 4 (2025). doi:10.7566/JPSJ.94.044802

  • P. D. Kolokathis, D. Zouraris, N. K. Sidiropoulos, A. Tsoumanis, G. Melagraki, I. Lynch, A. Afantitis, "NanoTube Construct: A web tool for the digital construction of nanotubes of single-layer materials and the calculation of their atomistic descriptors powered by Enalos Cloud Platform", Computational and Structural Biotechnology Journal, 25, 230-242 (2024). doi:10.1016/j.csbj.2024.09.023

  • P. D. Kolokathis, D. Zouraris, E. Voyiatzis, N. K. Sidiropoulos, A. Tsoumanis, G. Melagraki, K. Tämm, I. Lynch, A. Afantitis, "NanoConstruct: A web application builder of ellipsoidal nanoparticles for the investigation of their crystal growth, stability, and the calculation of atomistic descriptors", Computational and Structural Biotechnology Journal, 25, 81-90 (2024). doi:10.1016/j.csbj.2024.05.039

  • P. D. Kolokathis, E. Voyiatzis, N. K. Sidiropoulos, A. Tsoumanis, G. Melagraki, K. Tämm, I. Lynch, A. Afantitis, "ASCOT: A web tool for the digital construction of energy minimized Ag, CuO, TiO2 spherical nanoparticles and calculation of their atomistic descriptors", Computational and Structural Biotechnology Journal, 25, 34-46 (2024). doi:10.1016/j.csbj.2024.03.011

  • M. Lupo Pasini, M. Karabin, M. Eisenbach, "Transferring predictions of formation energy across lattices of increasing size", *Machine Learning: Science and Technology, 5, 2, 025015 (2024). doi:10.1088/2632-2153/ad3d2c

  • J. L. A. Gardner, K. T. Baker, V. L. Deringer, "Synthetic pre-training for neural-network interatomic potentials", Machine Learning: Science and Technology, 5, 1, 015003 (2024). doi:10.1088/2632-2153/ad1626

  • M. Li, G. Lu, H. Yu, M. Li, F. Zheng, "Scaling laws governing the elastic properties of 3D graphenes", Science China Technological Sciences, 67, 6, 1748-1756 (2024). doi:10.1007/s11431-023-2544-6

  • A. Siddiqui, N. D. M. Hine, "Machine-learned interatomic potentials for transition metal dichalcogenide Mo1−xWxS2−2ySe2y alloys", npj Computational Materials, 10, 1 (2024). doi:10.1038/s41524-024-01357-9

  • V. Venturi, R. Freitas, I. I. Abate, "Na vs. Li metal anodes for batteries: unraveling thermodynamic and electronic origins of voids and developing descriptors for artificial surface coatings", Journal of Materials Chemistry A, 12, 41, 27987-28001 (2024). doi:10.1039/d4ta00971a

  • A. Fisher, J. B. Staunton, H. Wu, P. Brommer, "First principles validation of energy barriers in Ni75Al25", Modelling and Simulation in Materials Science and Engineering, 32, 6, 065024 (2024). doi:10.1088/1361-651X/ad5c85

  • M. Karabin, M. Lupo Pasini, M. Eisenbach, "ORNL_AISD_NiPt", Constellation Dataset Repository (2023). doi:10.13139/OLCF/1958172

  • J. C. Verduzco, E. Holbrook, A. Strachan, "GPT-4 as an interface between researchers and computational software: improving usability and reproducibility", arXiv (2023). doi:10.48550/arXiv.2310.11458

  • X. Du, J. K. Damewood, J. R. Lunger, R. Millan, B. Yildiz, L. Li, R. Gómez-Bombarelli, "Machine-learning-accelerated simulations to enable automatic surface reconstruction", Nature Computational Science, 3, 12, 1034-1044 (2023). doi:10.1038/s43588-023-00571-7

  • D. Olson, C. Ortner, Y. Wang, L. Zhang, "Elastic Far-Field Decay from Dislocations in Multilattices", Multiscale Modeling & Simulation, 21, 4, 1379-1409 (2023). doi:10.1137/22M1502021

  • J. Chen, H. K. Yeddu, "Study of ageing and size effects in Nickel–Titanium shape memory alloy using molecular dynamics simulations", Phase Transitions, 96, 8, 596-606 (2023). doi:10.1080/01411594.2023.2235061

  • J. Chen, J. Nokelainen, B. Barbiellini, H. K. Yeddu, "Nanoscale phenomena during wetting of copper on nickel-based superalloy: A molecular dynamics study", Computational Materials Science, 230, 112453 (2023). doi:10.1016/j.commatsci.2023.112453

  • A. Farahvash, M. Agrawal, A. A. Peterson, A. P. Willard, "Modeling Surface Vibrations and Their Role in Molecular Adsorption: A Generalized Langevin Approach", Journal of Chemical Theory and Computation, 19, 18, 6452-6460 (2023). doi:10.1021/acs.jctc.3c00473

  • S. Burlison, M. F. Becker, D. Kovar, "A molecular dynamics study of the effects of velocity and diameter on the impact behavior of zinc oxide nanoparticles", Modelling and Simulation in Materials Science and Engineering, 31, 7, 075008 (2023). doi:10.1088/1361-651X/acf060

  • S. Nisany, D. Mordehai, "A Multiple Site Type Nucleation Model and Its Application to the Probabilistic Strength of Pd Nanowires", Metals, 12, 2, 280 (2022). doi:10.3390/met12020280

  • S. Homann, H. Luu, N. Merkert, "Molecular dynamics simulations of the machining of oxidized and deoxidized titanium work pieces", Results in Surfaces and Interfaces, 9, 100085 (2022). doi:10.1016/j.rsurfi.2022.100085

  • A. M. Barboza, L. C. Aliaga, D. Faria, I. N. Bastos, "Bilayer graphene kirigami", Carbon Trends, 9, 100227 (2022). doi:10.1016/j.cartre.2022.100227

  • D. Akzhigitov, T. Srymbetov, B. Golman, C. Spitas, Z. N. Utegulov, "Applied stress anisotropy effect on melting of tungsten: molecular dynamics study", Computational Materials Science, 204, 111139 (2022). doi:10.1016/j.commatsci.2021.111139

  • R. Bodlos, V. Fotopoulos, J. Spitaler, A. Shluger, L. Romaner, "Energies and structures of Cu/Nb and Cu/W interfaces from density functional theory and semi-empirical calculations", Materialia, 21, 101362 (2022). doi:10.1016/j.mtla.2022.101362

  • Y. Lysogorskiy, C. v. d. Oord, A. Bochkarev, S. Menon, M. Rinaldi, T. Hammerschmidt, M. Mrovec, A. Thompson, G. Csányi, C. Ortner, R. Drautz, "Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon", npj Computational Materials, 7, 1 (2021). doi:10.1038/s41524-021-00559-9

  • I. M. Padilla Espinosa, T. D. B. Jacobs, A. Martini, "Evaluation of Force Fields for Molecular Dynamics Simulations of Platinum in Bulk and Nanoparticle Forms", Journal of Chemical Theory and Computation, 17, 7, 4486-4498 (2021). doi:10.1021/acs.jctc.1c00434

  • D. Akzhigitov, T. Srymbetov, B. Golman, C. Spitas, Z. N. Utegulov, "Melting of tungsten under uniaxial and shear stresses: molecular dynamics simulation", Modelling and Simulation in Materials Science and Engineering, 28, 7, 075008 (2020). doi:10.1088/1361-651X/abaf39

  • S. T. Reeve, D. M. Guzman, L. Alzate-Vargas, B. Haley, P. Liao, A. Strachan, "Online simulation powered learning modules for materials science", MRS Advances, 4, 50, 2727-2742 (2019). doi:10.1557/adv.2019.287

  • L. Zhang, Y. Shibuta, X. Huang, C. Lu, M. Liu, "Grain boundary induced deformation mechanisms in nanocrystalline Al by molecular dynamics simulation: From interatomic potential perspective", Computational Materials Science, 156, 421-433 (2019). doi:10.1016/j.commatsci.2018.10.021

  • C. de Tomas, A. Aghajamali, J. L. Jones, D. J. Lim, M. J. López, I. Suarez-Martinez, N. A. Marks, "Transferability in interatomic potentials for carbon", Carbon, 155, 624-634 (2019). doi:10.1016/j.carbon.2019.07.074

  • N. C. Admal, J. Marian, G. Po, "The atomistic representation of first strain-gradient elastic tensors", Journal of the Mechanics and Physics of Solids, 99, 93-115 (2017). doi:10.1016/j.jmps.2016.11.005

  • J. Cho, J. Molinari, G. Anciaux, "Mobility law of dislocations with several character angles and temperatures in FCC aluminum", International Journal of Plasticity, 90, 66-75 (2017). doi:10.1016/j.ijplas.2016.12.004