Cover Image

High-Pressure Inelastic Neutron Spectroscopy: A true test of Machine-Learned Interatomic Potential energy landscapes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Armstrong, Jeff, Jackson, Adam, Elena, Alin
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

Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data
by: Han, Bowen, et al.
Published: (2025)

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

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

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

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

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

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

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

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

Importance of Electronic Entropy for Machine Learning Interatomic Potentials
by: Petersen, Martin Hoffmann, et al.
Published: (2026)

Universal Machine Learning Interatomic Potentials are Ready for Phonons
by: Loew, Antoine, et al.
Published: (2024)

Adaptive Loss Weighting for Machine Learning Interatomic Potentials
by: Ocampo, Daniel, 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)

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
by: Kim, Jaesun, 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)

Small-Cell-Based Fast Active Learning of Machine Learning Interatomic Potentials
by: Meng, Zijian, et al.
Published: (2025)

Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation for Training Machine-Learned Interatomic Potentials
by: Lahouari, Adam, et al.
Published: (2025)

PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
by: Koker, Teddy, et al.
Published: (2026)

MLIP-MC: A Framework for Adsorption Simulations using Machine-Learned Interatomic Potentials
by: Edwards, Connor W., et al.
Published: (2026)

Bias in Universal Machine-Learned Interatomic Potentials and its Effects on Fine-Tuning
by: Wong, Nicolas, et al.
Published: (2026)

Machine Learned Potential for High-Throughput Phonon Calculations of Metal-Organic Frameworks
by: Elena, Alin Marin, et al.
Published: (2024)

Inelastic Neutron Scattering for Direct Detection of Chiral Phonons
by: Wang, Tingting, et al.
Published: (2025)

Benchmarking Universal Machine-Learned Interatomic Potentials for High-Temperature Metal-Organic Framework Chemistry
by: Edwards, Connor W., et al.
Published: (2026)

Uni2D: A Universal Machine Learning Interatomic Potential for Two-Dimensional Materials
by: Wang, Haidi, et al.
Published: (2025)

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors
by: Du, Hongwei, et al.
Published: (2025)

Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials
by: Hattori, Shinnosuke, et al.
Published: (2025)

Machine Learning a Universal Harmonic Interatomic Potential for Predicting Phonons in Crystalline Solids
by: Lee, Huiju, et al.
Published: (2024)

Investigating Ionic Diffusivity in Amorphous Solid Electrolytes using Machine Learned Interatomic Potentials
by: Seth, Aqshat, et al.
Published: (2024)

Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
by: Maruf, Moin Uddin, et al.
Published: (2025)

A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations
by: Oh, Sangmin, et al.
Published: (2026)

A Robust Machine Learned Interatomic Potential for Nb: Collision Cascade Simulations with accurate Defect Configurations
by: Bhardwaj, Utkarsh, et al.
Published: (2025)

Predicting Interface Structure using the Minima Hopping Method with a Machine Learning Interatomic Potential
by: Chou, Chang-Ti, et al.
Published: (2026)

Tadah! A Swiss Army Knife for Developing and Deployment of Machine Learning Interatomic Potentials
by: Kirsz, M., et al.
Published: (2025)

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

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