Cover Image

Accurate and Efficient Interatomic Potentials for Dislocations in InP

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rocke, Thomas, Hudson, Thomas, Beanland, Richard, Kermode, James
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

Bayesian Selection for Efficient MLIP Dataset Selection
by: Rocke, Thomas, et al.
Published: (2025)

Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
by: Birks, Fraser, et al.
Published: (2025)

Bridging Atomistic and Continuum Descriptions of Nanoscale Dislocation Loops in Tungsten
by: Lopez, Joseph Duque, et al.
Published: (2026)

Uncertainty Quantification in Atomistic Simulations of Silicon using Interatomic Potentials
by: Best, I. R., et al.
Published: (2024)

Kink-Helium Interactions in Tungsten: Increased Dislocation Mobility in the Infinitely Dilute Regime
by: Nutter, Matthew, et al.
Published: (2024)

Superstructure reflexions in tilted perovskites Part 1
by: Beanland, Richard, et al.
Published: (2025)

Superstructure reflexions in tilted perovskites part 2
by: Beanland, Richard, et al.
Published: (2025)

Dislocation dynamics in Ni-based superalloys: Parameterising dislocation trajectories from atomistic simulations
by: Anis, Geraldine, et al.
Published: (2023)

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties
by: Hawthorne, Felipe, et al.
Published: (2025)

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials
by: Perez, Danny, et al.
Published: (2025)

Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems
by: Tahmasbi, Hossein, et al.
Published: (2025)

Study of Entropy-Driven Polymorphic Stability for Aspirin Using Accurate Neural Network Interatomic Potential
by: Hattori, Shinnosuke, et al.
Published: (2024)

Hydrogen under Pressure as a Benchmark for Machine-Learning Interatomic Potentials
by: Bischoff, Thomas, et al.
Published: (2024)

Large-Angle Convergent-Beam Electron Diffraction Patterns via Conditional Generative Adversarial Networks
by: Webb, Joseph J., et al.
Published: (2025)

Equivariant Many-body Message Passing Interatomic Potentials for Magnetic Materials
by: Ho, Cheuk Hin, et al.
Published: (2026)

Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials
by: Fragapane, Natascia L., et al.
Published: (2025)

Classical and Machine Learning Interatomic Potentials for BCC Vanadium
by: Wang, Rui, et al.
Published: (2022)

A "Magnetic" Machine Learning Interatomic Potential for Nickel
by: Gong, Xiaoguo, et al.
Published: (2023)

Equivariant Electronic Hamiltonian Prediction with Many-Body Message Passing
by: Qian, Chen, et al.
Published: (2025)

Machine-Learning Interatomic Potential for Twisted Hexagonal Boron Nitride: Accurate Structural Relaxation and Emergent Polarization
by: Luna, Wilson Nieto, et al.
Published: (2025)

Modelling Silica using MACE-MP-0 Machine Learnt Interatomic Potentials
by: Nasir, Jamal Abdul, et al.
Published: (2024)

Towards Accurate and Scalable High-throughput MOF Adsorption Screening: Merging Classical Force Fields and Universal Machine Learned Interatomic Potentials
by: Bonakala, Satyanarayana, et al.
Published: (2025)

Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials
by: Nong, Wei, et al.
Published: (2025)

Local Order Average-Atom Interatomic Potentials
by: Zeller, Chloe A., et al.
Published: (2025)

Parameter-Efficient Fine-Tuning of Machine-Learning Interatomic Potentials for Phonon and Thermal Properties
by: Grandel, Jonas, et al.
Published: (2026)

Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
by: Dominguez, Jon Eunan Quinlivan, et al.
Published: (2025)

Migration barriers for diffusion of As and P atoms in InP and InAs via vacancies and interstitial atoms
by: Aleksandrov, Ivan A., et al.
Published: (2024)

Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
by: Kang, Kisung, et al.
Published: (2024)

Improving Reliability of Machine Learned Interatomic Potentials With Physics-Informed Pretraining
by: Zheng, Qianyu, et al.
Published: (2026)

Machine Learning Interatomic Potentials for Million-Atom Simulations of Multicomponent Alloys
by: Shuang, Fei, et al.
Published: (2026)

Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects
by: Wang, Xinwei, et al.
Published: (2026)

Phosphorus-based lubricant additives on iron with Machine Learning Interatomic Potentials
by: Restuccia, Paolo, et al.
Published: (2025)

Molecular Dynamics Simulations of Collision Cascades in Niobium: Comparing Interatomic Potentials
by: Mondal, S., et al.
Published: (2025)

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
by: Kim, Jaesun, et al.
Published: (2025)

Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential
by: Sivak, Jacob T., et al.
Published: (2024)

Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning
by: Varughese, Bilvin, et al.
Published: (2025)

Benchmarking Universal Machine Learning Interatomic Potentials for Elastic Property Prediction
by: Gao, Pengfei, et al.
Published: (2025)

Machine Learned Interatomic Potentials for Ternary Carbides trained on the AFLOW Database
by: Roberts, Josiah, et al.
Published: (2024)

Evaluation of Foundational Machine Learned Interatomic Potentials for Migration Barrier Predictions
by: Bheemaguli, Achinthya Krishna, et al.
Published: (2025)

PySlice: Routine Vibrational Electron Energy Loss Spectroscopy Prediction with Universal Interatomic Potentials
by: Walker, Harrison A., et al.
Published: (2026)