Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials
Fuente:
arXiv
Saved in:
| Main Authors: | Fragapane, Natascia L., Deringer, Volker L. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
An automated framework for exploring and learning potential-energy surfaces
by: Liu, Yuanbin, et al.
Published: (2024)
by: Liu, Yuanbin, et al.
Published: (2024)
Universal Machine Learning Interatomic Potentials are Ready for Phonons
by: Loew, Antoine, et al.
Published: (2024)
by: Loew, Antoine, et al.
Published: (2024)
Hyperparameter Optimization for Atomic Cluster Expansion Potentials
by: Toit, Daniel F. Thomas du, et al.
Published: (2024)
by: Toit, Daniel F. Thomas du, et al.
Published: (2024)
The Zintl-Klemm Concept in the Amorphous State: A Case Study of Na-P Battery Anodes
by: Wu, Litong, et al.
Published: (2025)
by: Wu, Litong, et al.
Published: (2025)
Atomic cluster expansion potential for the Si-H system
by: Rosset, Louise A. M., et al.
Published: (2025)
by: Rosset, Louise A. M., 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)
Adaptive Loss Weighting for Machine Learning Interatomic Potentials
by: Ocampo, Daniel, et al.
Published: (2024)
by: Ocampo, Daniel, et al.
Published: (2024)
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)
Predicting Crystal Structures and Ionic Conductivities in Li$_{3}$YCl$_{6-x}$Br$_{x}$ Halide Solid Electrolytes Using a Fine-Tuned Machine Learning Interatomic Potential
by: Böhm, Jonas, et al.
Published: (2025)
by: Böhm, Jonas, 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)
Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces
by: Focassio, Bruno, et al.
Published: (2024)
by: Focassio, Bruno, et al.
Published: (2024)
Tadah! A Swiss Army Knife for Developing and Deployment of Machine Learning Interatomic Potentials
by: Kirsz, M., et al.
Published: (2025)
by: Kirsz, M., et al.
Published: (2025)
Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS2
by: Faiyad, Abrar, et al.
Published: (2025)
by: Faiyad, Abrar, 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)
by: Grandel, Jonas, et al.
Published: (2026)
Target-Distribution-Guided Cross-Functional Fine-Tuning of Machine-Learning Interatomic Potentials
by: Nagai, Yuki, et al.
Published: (2026)
by: Nagai, Yuki, et al.
Published: (2026)
MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform
by: Chiang, Yuan, et al.
Published: (2025)
by: Chiang, Yuan, et al.
Published: (2025)
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)
Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Towards a Universal Model
by: Liu, Jianchuan, et al.
Published: (2023)
by: Liu, Jianchuan, et al.
Published: (2023)
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)
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)
Modeling the Behavior of Complex Aqueous Electrolytes Using Machine Learning Interatomic Potentials: The Case of Sodium Sulfate
by: Soyemi, Ademola, et al.
Published: (2025)
by: Soyemi, Ademola, et al.
Published: (2025)
Robustness of Local Predictions in Atomistic Machine Learning Models
by: Chong, Sanggyu, et al.
Published: (2023)
by: Chong, Sanggyu, et al.
Published: (2023)
Local Order Average-Atom Interatomic Potentials
by: Zeller, Chloe A., et al.
Published: (2025)
by: Zeller, Chloe A., et al.
Published: (2025)
Full-cycle device-scale simulations of memory materials with a tailored atomic-cluster-expansion potential
by: Zhou, Yuxing, et al.
Published: (2025)
by: Zhou, Yuxing, et al.
Published: (2025)
Accuracy and Limitations of Machine-Learned Interatomic Potentials for Magnetic Systems: A Case Study on Fe-Cr-C
by: Khazieva, E. O., et al.
Published: (2025)
by: Khazieva, E. O., 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)
Large-Scale, Long-Time Atomistic Simulations of Proton Transport in Polymer Electrolyte Membranes Using a Neural Network Interatomic Potential
by: Yoshimoto, Yuta, et al.
Published: (2025)
by: Yoshimoto, Yuta, et al.
Published: (2025)
Graph Neural Network for Unified Electronic and Interatomic Potentials: Strain-tunable Electronic Structures in 2D Materials
by: Choi, Moon-ki, et al.
Published: (2025)
by: Choi, Moon-ki, et al.
Published: (2025)
MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling
by: Kraß, Hendrik, et al.
Published: (2025)
by: Kraß, Hendrik, et al.
Published: (2025)
Construction and Tuning of CALPHAD Models Using Machine-Learned Interatomic Potentials and Experimental Data: A Case Study of the Pt-W System
by: Kunselman, Courtney, et al.
Published: (2025)
by: Kunselman, Courtney, et al.
Published: (2025)
Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials
by: Ko, Tsz Wai, et al.
Published: (2024)
by: Ko, Tsz Wai, et al.
Published: (2024)
Machine-learned Interatomic Potential for Ti$_{n+1}$C$_n$ MXenes: Application to Ion Irradiation Simulations
by: Byggmästar, Jesper
Published: (2026)
by: Byggmästar, Jesper
Published: (2026)
Graph-neural-network predictions of solid-state NMR parameters from spherical tensor decomposition
by: Mahmoud, Chiheb Ben, et al.
Published: (2024)
by: Mahmoud, Chiheb Ben, et al.
Published: (2024)
Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows
by: Li, Wenwen, et al.
Published: (2026)
by: Li, Wenwen, et al.
Published: (2026)
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)
Matlantis-PFP v8: Universal Machine Learning Interatomic Potential with Better Experimental Agreements via r2SCAN Functional
by: Shinagawa, Chikashi, et al.
Published: (2026)
by: Shinagawa, Chikashi, et al.
Published: (2026)
Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems
by: Tahmasbi, Hossein, et al.
Published: (2025)
by: Tahmasbi, Hossein, et al.
Published: (2025)
Random Spin Committee Approach For Smooth Interatomic Potentials
by: Cărare, Vlad, et al.
Published: (2024)
by: Cărare, Vlad, et al.
Published: (2024)
The Microscopic Nature of Orbital Disorder in LaMnO$_{3}$
by: Batnaran, Bodoo, et al.
Published: (2025)
by: Batnaran, Bodoo, et al.
Published: (2025)
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)
Similar Items
-
An automated framework for exploring and learning potential-energy surfaces
by: Liu, Yuanbin, et al.
Published: (2024) -
Universal Machine Learning Interatomic Potentials are Ready for Phonons
by: Loew, Antoine, et al.
Published: (2024) -
Hyperparameter Optimization for Atomic Cluster Expansion Potentials
by: Toit, Daniel F. Thomas du, et al.
Published: (2024) -
The Zintl-Klemm Concept in the Amorphous State: A Case Study of Na-P Battery Anodes
by: Wu, Litong, et al.
Published: (2025) -
Atomic cluster expansion potential for the Si-H system
by: Rosset, Louise A. M., et al.
Published: (2025)