Benchmarking Universal Machine Learning Interatomic Potentials for Elastic Property Prediction
Fuente:
arXiv
Saved in:
| Main Authors: | Gao, Pengfei, Wang, Haidi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems
by: Tahmasbi, Hossein, et al.
Published: (2025)
by: Tahmasbi, Hossein, et al.
Published: (2025)
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)
by: Volkmer, Lukas, et al.
Published: (2025)
Universal Machine Learning Interatomic Potentials are Ready for Phonons
by: Loew, Antoine, et al.
Published: (2024)
by: Loew, Antoine, et al.
Published: (2024)
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)
Evaluating Mechanical Property Prediction across Material Classes using Molecular Dynamics Simulations with Universal Machine-Learned Interatomic Potentials
by: Stracke, Konstantin, et al.
Published: (2025)
by: Stracke, Konstantin, 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)
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)
UniMatSim: A High-Throughput Materials Simulation Automation Framework Based on Universal Machine Learning Potentials
by: Xiang, Yanjin, et al.
Published: (2026)
by: Xiang, Yanjin, et al.
Published: (2026)
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)
Benchmarking Universal Machine-Learned Interatomic Potentials for High-Temperature Metal-Organic Framework Chemistry
by: Edwards, Connor W., et al.
Published: (2026)
by: Edwards, Connor W., et al.
Published: (2026)
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)
Classical and Machine Learning Interatomic Potentials for BCC Vanadium
by: Wang, Rui, et al.
Published: (2022)
by: Wang, Rui, et al.
Published: (2022)
Adaptive Loss Weighting for Machine Learning Interatomic Potentials
by: Ocampo, Daniel, et al.
Published: (2024)
by: Ocampo, Daniel, et al.
Published: (2024)
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)
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)
Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering Data
by: Han, Bowen, et al.
Published: (2025)
by: Han, Bowen, et al.
Published: (2025)
Benchmarking Universal Interatomic Potentials on Zeolite Structures
by: Ito, Shusuke, et al.
Published: (2025)
by: Ito, Shusuke, et al.
Published: (2025)
Accelerating Amorphous Alloy Discovery: Data-Driven Property Prediction via General-Purpose Machine Learning Interatomic Potential
by: Gong, Xuhe, et al.
Published: (2025)
by: Gong, Xuhe, et al.
Published: (2025)
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)
Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials
by: Perez, Danny, et al.
Published: (2025)
by: Perez, Danny, et al.
Published: (2025)
A "Magnetic" Machine Learning Interatomic Potential for Nickel
by: Gong, Xiaoguo, et al.
Published: (2023)
by: Gong, Xiaoguo, et al.
Published: (2023)
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)
Benchmarking CHGNet Universal Machine Learning Interatomic Potential Against DFT and EXAFS: Case of Layered WS2 and MoS2
by: Žguns, Pjotrs, et al.
Published: (2025)
by: Žguns, Pjotrs, et al.
Published: (2025)
Benchmarking Universal Machine Learning Interatomic Potentials for Supported Nanoparticles: Decoupling Energy Accuracy from Structural Exploration
by: Xu, Jiayan, et al.
Published: (2025)
by: Xu, Jiayan, 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)
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)
Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects
by: Wang, Xinwei, et al.
Published: (2026)
by: Wang, Xinwei, et al.
Published: (2026)
Parameter-Efficient Fine-Tuning of Machine-Learning Interatomic Potentials for Phonon and Thermal Properties
by: Grandel, Jonas, et al.
Published: (2026)
by: Grandel, Jonas, et al.
Published: (2026)
Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
by: Kuroda, Naoya, et al.
Published: (2026)
by: Kuroda, Naoya, 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)
by: Mehdizadeh, Ardavan, 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)
by: Oh, Sangmin, et al.
Published: (2026)
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)
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)
Improving Reliability of Machine Learned Interatomic Potentials With Physics-Informed Pretraining
by: Zheng, Qianyu, et al.
Published: (2026)
by: Zheng, Qianyu, et al.
Published: (2026)
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)
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)
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)
Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties
by: Hawthorne, Felipe, et al.
Published: (2025)
by: Hawthorne, Felipe, 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)
Similar Items
-
Uni2D: A Universal Machine Learning Interatomic Potential for Two-Dimensional Materials
by: Wang, Haidi, et al.
Published: (2025) -
Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems
by: Tahmasbi, Hossein, et al.
Published: (2025) -
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) -
Universal Machine Learning Interatomic Potentials are Ready for Phonons
by: Loew, Antoine, et al.
Published: (2024) -
Machine Learning a Universal Harmonic Interatomic Potential for Predicting Phonons in Crystalline Solids
by: Lee, Huiju, et al.
Published: (2024)