Cover Image

Machine learning interatomic potentials for solid-state precipitation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Piersante, Lorenzo, Natarajan, Anirudh Raju
Format:	Preprint
Published:	2026
Subjects:	Materials Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Synthesizability, hardness, and stacking order in multicomponent transition metal carbides from machine-learned potentials
by: Liu, Xin, et al.
Published: (2026)

Constructing multicomponent cluster expansions with machine-learning and chemical embedding
by: Müller, Yann L., et al.
Published: (2024)

Diffusion coefficients of multi-principal element alloys from first principles
by: Lee, Damien K. J., et al.
Published: (2026)

An AI-ready fine-tuning framework for accurate machine-learning interatomic potentials in solid-solid battery interfaces
by: Liu, Xiaoqing, et al.
Published: (2026)

Machine learning interatomic potential for predicting the thermal properties of uranium nitride
by: Chen, Beihan, et al.
Published: (2025)

Machine-learning interatomic potential for AlN for epitaxial simulation
by: Taormina, Nicholas, et al.
Published: (2025)

Platonic representation of foundation machine learning interatomic potentials
by: Li, Zhenzhu, et al.
Published: (2025)

Cartesian atomic moment machine learning interatomic potentials
by: Wen, Mingjian, et al.
Published: (2024)

Benchmarking phonon anharmonicity in machine learning interatomic potentials
by: Bandi, Sasaank, et al.
Published: (2024)

Modeling the Equilibrium Vacancy Concentration in Multi-Principal Element Alloys from First-Principles
by: Lee, Damien K. J., et al.
Published: (2025)

Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025)

Toward machine learning interatomic potentials for modeling uranium mononitride
by: Alzate-Vargas, Lorena, et al.
Published: (2024)

Systematic assessment of various universal machine-learning interatomic potentials
by: Yu, Haochen, et al.
Published: (2024)

Exploring the energy landscape of aluminas through machine learning interatomic potential
by: Zhang, Lei, et al.
Published: (2024)

Graph atomic cluster expansion for foundational machine learning interatomic potentials
by: Lysogorskiy, Yury, et al.
Published: (2025)

Atomistic modeling of uranium monocarbide with a machine learning interatomic potential
by: Alzate-Vargas, Lorena, et al.
Published: (2025)

Machine-learning interatomic potentials from a users perspective: A comparison of accuracy, speed and data efficiency
by: Leimeroth, Niklas, et al.
Published: (2025)

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
by: Kang, Hanwen, et al.
Published: (2025)

Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li$_6$PS$_5$Cl using machine-learning interatomic potentials
by: Ou, Yongliang, et al.
Published: (2024)

Machine-learning interatomic potentials achieving CCSD(T) accuracy for systems with extended covalent networks and van der Waals interactions
by: Ikeda, Yuji, et al.
Published: (2025)

Cross-functional transferability in universal machine learning interatomic potentials
by: Huang, Xu, et al.
Published: (2025)

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
by: Noordhoek, Kyle, et al.
Published: (2023)

Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials
by: Parackal, Abhijith S, et al.
Published: (2026)

Triplet Envelope Functions for increasing machine learning interatomic potential efficiency and stability
by: Annevelink, Emil, et al.
Published: (2026)

KLIFF: A framework to develop physics-based and machine learning interatomic potentials
by: Wen, Mingjian, et al.
Published: (2021)

Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning
by: Radova, Mariia, et al.
Published: (2025)

Fast machine learned $α$-Fe-H interatomic potential for hydrogen embrittlement
by: Makkonen, Eetu, et al.
Published: (2025)

Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability
by: Mukhamedov, Boburjon, et al.
Published: (2024)

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials
by: Moon, Minseok, et al.
Published: (2025)

Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium
by: Fletcher, Vincent G., et al.
Published: (2025)

Minimalist machine-learned interatomic potentials can predict complex structural behaviors accurately
by: Robredo-Magro, Iñigo, et al.
Published: (2025)

Data-efficient multi-fidelity training for high-fidelity machine learning interatomic potentials
by: Kim, Jaesun, et al.
Published: (2024)

Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials
by: Subramanyam, Aparna P. A., et al.
Published: (2024)

Thermal boundary conductance of metal diamond interfaces predicted by machine learning interatomic potentials
by: Adnan, Khalid Zobaid, et al.
Published: (2024)

Nine-element machine-learned interatomic potentials for multiphase refractory alloys
by: Byggmästar, Jesper, et al.
Published: (2026)

Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials
by: Georgaras, Johnathan D., et al.
Published: (2025)

Screening of material defects using universal machine-learning interatomic potentials
by: Berger, Ethan, et al.
Published: (2025)

A practical guide to machine learning interatomic potentials -- Status and future
by: Jacobs, Ryan, et al.
Published: (2025)

Thermodynamic and electronic properties of rutile Sn$_{1-x}$Ge$_x$O$_2$ alloys from first principles
by: Müller, Yann L., et al.
Published: (2026)

Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)