Cover Image

Geometry-enhanced Pre-training on Interatomic Potentials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cui, Taoyong, Tang, Chenyu, Su, Mao, Zhang, Shufei, Li, Yuqiang, Bai, Lei, Dong, Yuhan, Gong, Xingao, Ouyang, Wanli
Format:	Preprint
Published:	2023
Subjects:	Chemical Physics Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Online Test-time Adaptation for Interatomic Potentials
by: Cui, Taoyong, et al.
Published: (2024)

Evidential Deep Learning for Interatomic Potentials
by: Xu, Han, et al.
Published: (2024)

Iterative Pretraining Framework for Interatomic Potentials
by: Cui, Taoyong, et al.
Published: (2025)

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials
by: Cui, Taoyong, et al.
Published: (2025)

An SO(3)-equivariant reciprocal-space neural potential for long-range interactions
by: Zhang, Lingfeng, et al.
Published: (2026)

Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
by: Brunken, Christoph, et al.
Published: (2025)

Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
by: Ho, Cheuk Hin, et al.
Published: (2025)

Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
by: Dominguez, Jon Eunan Quinlivan, et al.
Published: (2025)

Equivariant Evidential Deep Learning for Interatomic Potentials
by: Wang, Zhongyao, et al.
Published: (2026)

MLIPAudit: A benchmarking tool for Machine Learned Interatomic Potentials
by: Wehrhan, Leon, et al.
Published: (2025)

False Metallization in Short-Ranged Machine Learned Interatomic Potentials
by: Parker, Isaac J., et al.
Published: (2026)

Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
by: Chong, Sanggyu, et al.
Published: (2025)

Linear Scaling Calculation of Atomic Forces and Energies with Machine Learning Local Density Matrix
by: Xin, Zaizhou, et al.
Published: (2025)

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials
by: Fu, Cong, et al.
Published: (2025)

DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials
by: Midgley, Laurence I., et al.
Published: (2026)

Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
by: Goodwin, Zachary A. H., et al.
Published: (2024)

Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials
by: Zhang, Yaolong, et al.
Published: (2025)

Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials
by: Schäfer, Moritz René, et al.
Published: (2025)

Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins
by: Zeng, Lejia, et al.
Published: (2026)

Reliable and Efficient Automated Transition-State Searches with Machine-Learned Interatomic Potentials
by: Marks, Jonah, et al.
Published: (2026)

Scaling Machine Learning Interatomic Potentials with Mixtures of Experts
by: Liu, Yuzhi, et al.
Published: (2026)

Physics-Informed Weakly Supervised Learning for Interatomic Potentials
by: Takamoto, Makoto, et al.
Published: (2024)

Enhanced Representation-Based Sampling for the Efficient Generation of Datasets for Machine-Learned Interatomic Potentials
by: Schäfer, Moritz René, et al.
Published: (2026)

Shoot from the HIP: Hessian Interatomic Potentials without derivatives
by: Burger, Andreas, et al.
Published: (2025)

Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
by: Matin, Sakib, et al.
Published: (2025)

Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis
by: Karimitari, Nima, et al.
Published: (2026)

Machine-Learning Interatomic Potentials for Long-Range Systems
by: Ji, Yajie, et al.
Published: (2025)

Fine-Tuning Unifies Foundational Machine-learned Interatomic Potential Architectures at ab initio Accuracy
by: Hänseroth, Jonas, et al.
Published: (2025)

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential
by: Kumar, Nitesh, et al.
Published: (2026)

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
by: Brunken, Christoph, et al.
Published: (2026)

Predicting Spectroscopic Properties of Solvated Nile Red with Automated Workflows for Machine Learned Interatomic Potentials
by: Eller, Jacob, et al.
Published: (2025)

Recent Advances in Unconventional Ferroelectrics and Multiferroics
by: Yu, Hongyu, et al.
Published: (2025)

A Pre-trained Deep Potential Model for Sulfide Solid Electrolytes with Broad Coverage and High Accuracy
by: Wang, Ruoyu, et al.
Published: (2024)

Data-Efficient Construction of High-Fidelity Graph Deep Learning Interatomic Potentials
by: Ko, Tsz Wai, et al.
Published: (2024)

Random Spin Committee Approach For Smooth Interatomic Potentials
by: Cărare, Vlad, et al.
Published: (2024)

MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials
by: Osaro, Etinosa, et al.
Published: (2026)

Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
by: Baldwin, William J., et al.
Published: (2026)

Liquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl3) Enabled by Machine Learning Interatomic Potentials
by: Chahal, Rajni, et al.
Published: (2024)

QCBench: Evaluating Large Language Models on Domain-Specific Quantitative Chemistry
by: Xie, Jiaqing, et al.
Published: (2025)

Benchmarking Universal Interatomic Potentials on Zeolite Structures
by: Ito, Shusuke, et al.
Published: (2025)