Cover Image

Machine Learning a Universal Harmonic Interatomic Potential for Predicting Phonons in Crystalline Solids

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lee, Huiju, Xia, Yi
Format:	Preprint
Published:	2024
Subjects:	Materials Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Accelerating High-Throughput Phonon Calculations via Machine Learning Universal Potentials
by: Lee, Huiju, et al.
Published: (2024)

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

Data-Driven Exploration and Insights into Temperature-Dependent Phonons in Inorganic Materials
by: Lee, Huiju, et al.
Published: (2026)

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

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

Benchmarking Universal Machine Learning Interatomic Potentials for Elastic Property Prediction
by: Gao, Pengfei, 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 on Elemental Systems
by: Tahmasbi, Hossein, 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)

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

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

Ligand-Controlled Phonon Dynamics in CsPbBr3 Nanocrystals Revealed by Machine-Learned Interatomic Potentials
by: Cha, Seungjun, et al.
Published: (2026)

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

High-throughput computational framework for lattice dynamics and thermal transport including high-order anharmonicity: an application to cubic and tetragonal inorganic compounds
by: Li, Zhi, 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)

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

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)

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

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

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

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials
by: Miret, Santiago, et al.
Published: (2025)

Evaluating Mechanical Property Prediction across Material Classes using Molecular Dynamics Simulations with Universal Machine-Learned Interatomic Potentials
by: Stracke, Konstantin, et al.
Published: (2025)

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

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

On-the-Fly Machine Learning of Interatomic Potentials for Elastic Property Modeling in Al-Mg-Zr Solid Solutions
by: Volkmer, Lukas, et al.
Published: (2025)

Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
by: Kuroda, Naoya, et al.
Published: (2026)

Adaptive Loss Weighting for Machine Learning Interatomic Potentials
by: Ocampo, Daniel, et al.
Published: (2024)

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

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

Machine-Learned Interatomic Potentials for Predicting Physicochemical Properties of Molten Metal-Salt Systems for Calcium Electrolysis
by: Polovinkin, M., et al.
Published: (2026)

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

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)

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

Revealing Phonon Bridge Effect for Amorphous vs Crystalline Metal-Silicide Layers at Si/Ti Interfaces by a Machine Learning Potential
by: Singh, Mayur, et al.
Published: (2025)

Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity
by: Hu, Yanxiao, 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)

Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Towards a Universal Model
by: Liu, Jianchuan, et al.
Published: (2023)