Improving Reliability of Machine Learned Interatomic Potentials With Physics-Informed Pretraining
Fuente:
arXiv
Saved in:
| Main Authors: | Zheng, Qianyu, Fung, Victor |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Ligand-Controlled Phonon Dynamics in CsPbBr3 Nanocrystals Revealed by Machine-Learned Interatomic Potentials
by: Cha, Seungjun, et al.
Published: (2026)
by: Cha, Seungjun, et al.
Published: (2026)
Scalable Foundation Interatomic Potentials via Message-Passing Pruning and Graph Partitioning
by: Kong, Lingyu, et al.
Published: (2025)
by: Kong, Lingyu, 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)
by: Kang, Kisung, et al.
Published: (2024)
Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices
by: Maxson, Tristan, et al.
Published: (2024)
by: Maxson, Tristan, et al.
Published: (2024)
Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials
by: Perez, Danny, et al.
Published: (2025)
by: Perez, Danny, et al.
Published: (2025)
Classical and Machine Learning Interatomic Potentials for BCC Vanadium
by: Wang, Rui, et al.
Published: (2022)
by: Wang, Rui, et al.
Published: (2022)
A "Magnetic" Machine Learning Interatomic Potential for Nickel
by: Gong, Xiaoguo, et al.
Published: (2023)
by: Gong, Xiaoguo, et al.
Published: (2023)
Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems
by: Tahmasbi, Hossein, et al.
Published: (2025)
by: Tahmasbi, Hossein, et al.
Published: (2025)
Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials
by: Nong, Wei, et al.
Published: (2025)
by: Nong, Wei, et al.
Published: (2025)
Importance of Electronic Entropy for Machine Learning Interatomic Potentials
by: Petersen, Martin Hoffmann, et al.
Published: (2026)
by: Petersen, Martin Hoffmann, et al.
Published: (2026)
Universal Machine Learning Interatomic Potentials are Ready for Phonons
by: Loew, Antoine, et al.
Published: (2024)
by: Loew, Antoine, et al.
Published: (2024)
Adaptive Loss Weighting for Machine Learning Interatomic Potentials
by: Ocampo, Daniel, et al.
Published: (2024)
by: Ocampo, Daniel, et al.
Published: (2024)
Machine Learning Interatomic Potentials for Million-Atom Simulations of Multicomponent Alloys
by: Shuang, Fei, et al.
Published: (2026)
by: Shuang, Fei, et al.
Published: (2026)
Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects
by: Wang, Xinwei, et al.
Published: (2026)
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)
by: Restuccia, Paolo, et al.
Published: (2025)
Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
by: Kim, Jaesun, et al.
Published: (2025)
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)
by: Sivak, Jacob T., et al.
Published: (2024)
Benchmarking Universal Machine Learning Interatomic Potentials for Elastic Property Prediction
by: Gao, Pengfei, et al.
Published: (2025)
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)
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)
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)
by: Meng, Zijian, et al.
Published: (2025)
PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
by: Koker, Teddy, et al.
Published: (2026)
by: Koker, Teddy, et al.
Published: (2026)
Bias in Universal Machine-Learned Interatomic Potentials and its Effects on Fine-Tuning
by: Wong, Nicolas, et al.
Published: (2026)
by: Wong, Nicolas, et al.
Published: (2026)
Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning
by: Varughese, Bilvin, et al.
Published: (2025)
by: Varughese, Bilvin, et al.
Published: (2025)
Hydrogen under Pressure as a Benchmark for Machine-Learning Interatomic Potentials
by: Bischoff, Thomas, et al.
Published: (2024)
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)
by: Du, Hongwei, et al.
Published: (2025)
MLIP-MC: A Framework for Adsorption Simulations using Machine-Learned Interatomic Potentials
by: Edwards, Connor W., et al.
Published: (2026)
by: Edwards, Connor W., et al.
Published: (2026)
Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials
by: Hattori, Shinnosuke, et al.
Published: (2025)
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)
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)
by: Seth, Aqshat, et al.
Published: (2024)
Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
by: Maruf, Moin Uddin, et al.
Published: (2025)
by: Maruf, Moin Uddin, 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)
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)
by: Wang, Haidi, et al.
Published: (2025)
Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
by: Birks, Fraser, et al.
Published: (2025)
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)
by: Fragapane, Natascia L., 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)
by: Nascimento, Gabriel de Miranda, et al.
Published: (2026)
Modelling Silica using MACE-MP-0 Machine Learnt Interatomic Potentials
by: Nasir, Jamal Abdul, et al.
Published: (2024)
by: Nasir, Jamal Abdul, 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)
by: Polovinkin, M., et al.
Published: (2026)
A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations
by: Oh, Sangmin, et al.
Published: (2026)
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)
by: Bhardwaj, Utkarsh, et al.
Published: (2025)
Similar Items
-
Ligand-Controlled Phonon Dynamics in CsPbBr3 Nanocrystals Revealed by Machine-Learned Interatomic Potentials
by: Cha, Seungjun, et al.
Published: (2026) -
Scalable Foundation Interatomic Potentials via Message-Passing Pruning and Graph Partitioning
by: Kong, Lingyu, 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) -
Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices
by: Maxson, Tristan, et al.
Published: (2024) -
Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials
by: Perez, Danny, et al.
Published: (2025)