Cover Image

PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Koker, Teddy, Gangan, Abhijeet, Kotak, Mit, Marian, Jaime, Smidt, Tess
Format:	Preprint
Published:	2026
Subjects:	Materials Science Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Training a Foundation Model for Materials on a Budget
by: Koker, Teddy, et al.
Published: (2025)

Asymptotically Fast Clebsch-Gordan Tensor Products with Vector Spherical Harmonics
by: Xie, YuQing, et al.
Published: (2026)

The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products
by: Xie, YuQing, et al.
Published: (2025)

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

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

Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials
by: Koker, Teddy, et al.
Published: (2023)

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

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

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials
by: Shuang, Fei, 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)

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
by: Nascimento, Gabriel de Miranda, et al.
Published: (2026)

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

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)

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

Cutting Through the Noise: On-the-fly Outlier Detection for Robust Training of Machine Learning Interatomic Potentials
by: Lam, Terry C. W., et al.
Published: (2026)

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

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

MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling
by: Kraß, Hendrik, et al.
Published: (2025)

Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows
by: Li, Wenwen, et al.
Published: (2026)

Surface Stability Modeling with Universal Machine Learning Interatomic Potentials: A Comprehensive Cleavage Energy Benchmarking Study
by: Mehdizadeh, Ardavan, et al.
Published: (2025)

DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
by: Han, Kevin, et al.
Published: (2025)

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
by: Liu, Ryan, et al.
Published: (2026)

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

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
by: Benítez, Pol, et al.
Published: (2025)

Phonon predictions with E(3)-equivariant graph neural networks
by: Fang, Shiang, et al.
Published: (2024)

Flexible Cutoff Learning: Optimizing Machine Learning Potentials After Training
by: Oerder, Rick, et al.
Published: (2026)

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

Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements
by: Röcken, Sebastien, et al.
Published: (2025)

Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality
by: Hossain, Sazzad, et al.
Published: (2025)

Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory
by: Yu, Benjamin, et al.
Published: (2025)

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

P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for Machine Learning Potentials
by: Huang, Shih-Peng, et al.
Published: (2025)

Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials
by: Basu, Abhinaba, et al.
Published: (2026)

Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials
by: Nourollah, Amir Masoud, et al.
Published: (2026)

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)

Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design
by: Li, Jiaxiang, et al.
Published: (2025)

Target-Distribution-Guided Cross-Functional Fine-Tuning of Machine-Learning Interatomic Potentials
by: Nagai, Yuki, et al.
Published: (2026)

Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges
by: Zhu, Siya, et al.
Published: (2024)