Image de couverture de livre

Predicting Interface Structure using the Minima Hopping Method with a Machine Learning Interatomic Potential

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Chou, Chang-Ti, Wang, Menghang, Yang, Chao, van Aken, Peter A., Perry, Nicola H., Kozinsky, Boris, Wolverton, Christopher M.
Format:	Preprint
Publié:	2026
Sujets:	Materials Science
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Incongruent Melting and Phase Diagram of SiC from Machine Learning Molecular Dynamics
par: Xie, Yu, et autres
Publié: (2025)

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
par: Nascimento, Gabriel de Miranda, et autres
Publié: (2026)

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

Nonlocal Machine-Learned Exchange Functional for Molecules and Solids
par: Bystrom, Kyle, et autres
Publié: (2023)

DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials
par: Midgley, Laurence I., et autres
Publié: (2026)

Addressing the Band Gap Problem with a Machine-Learned Exchange Functional
par: Bystrom, Kyle, et autres
Publié: (2024)

Coupled reaction and diffusion governing interface evolution in solid-state batteries
par: Ding, Jingxuan, et autres
Publié: (2025)

Quantum theory of nonlinear phononics
par: Libbi, Francesco, et autres
Publié: (2025)

Modeling Equilibrium Solid-liquid Interfaces under Effective Constant Chemical Potential using Machine Learning Interatomic Potentials
par: Soyemi, Ademola, et autres
Publié: (2025)

Machine Learning Interatomic Potential for Alumina
par: Galib, Musanna
Publié: (2025)

Improving Molecular‐Dynamics Simulations for Solid–Liquid Interfaces with Machine‐Learning Interatomic Potentials
par: Pengfei Hou, et autres
Publié: (2024)

Benchmarking Universal Machine Learning Interatomic Potentials for Elastic Property Prediction
par: Gao, Pengfei, et autres
Publié: (2025)

Evaluation of Foundational Machine Learned Interatomic Potentials for Migration Barrier Predictions
par: Bheemaguli, Achinthya Krishna, et autres
Publié: (2025)

Hierarchical Crystal Structure Prediction of Zeolitic Imidazolate Frameworks Using DFT and Machine-Learned Interatomic Potentials
par: Xu, Yizhi, et autres
Publié: (2026)

Screening of Material Defects using Universal Machine‐Learning Interatomic Potentials
par: Ethan Berger, et autres
Publié: (2025)

Machine Learning Interatomic Potentials with Keras API
par: Rili, James Paolo
Publié: (2024)

Machine Learning Interatomic Potentials for Heterogeneous Catalysis
par: Deqi Tang, et autres
Publié: (2024)

Extrapolation of Machine-Learning Interatomic Potentials for Organic and Polymeric Systems
par: Hooven, Natalie E., et autres
Publié: (2025)

Accelerating High-Throughput Phonon Calculations via Machine Learning Universal Potentials
par: Lee, Huiju, et autres
Publié: (2024)

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
par: Liu, Xiaoqing, et autres
Publié: (2025)

Modelling Silica using MACE-MP-0 Machine Learnt Interatomic Potentials
par: Nasir, Jamal Abdul, et autres
Publié: (2024)

Benchmarking Universal Interatomic Potentials on Zeolite Structures
par: Ito, Shusuke, et autres
Publié: (2025)

Machine Learning a Universal Harmonic Interatomic Potential for Predicting Phonons in Crystalline Solids
par: Lee, Huiju, et autres
Publié: (2024)

Small-Cell-Based Fast Active Learning of Machine Learning Interatomic Potentials
par: Meng, Zijian, et autres
Publié: (2025)

Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
par: Matin, Sakib, et autres
Publié: (2025)

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials
par: Perez, Danny, et autres
Publié: (2025)

Classical and Machine Learning Interatomic Potentials for BCC Vanadium
par: Wang, Rui, et autres
Publié: (2022)

Universal Machine Learning Interatomic Potentials are Ready for Phonons
par: Loew, Antoine, et autres
Publié: (2024)

Smooth Dynamic Cutoffs for Machine Learning Interatomic Potentials
par: Han, Kevin, et autres
Publié: (2026)

Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
par: Ho, Cheuk Hin, et autres
Publié: (2025)

Importance of Electronic Entropy for Machine Learning Interatomic Potentials
par: Petersen, Martin Hoffmann, et autres
Publié: (2026)

Machine-Learning Interatomic Potentials for Long-Range Systems
par: Ji, Yajie, et autres
Publié: (2025)

Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials
par: Nourollah, Amir Masoud, et autres
Publié: (2026)

Machine Learning Interatomic Potential‐Enabled Discovery of Chlorofullerenes
par: Zi‐Yang Qiu, et autres
Publié: (2025)

Scaling Machine Learning Interatomic Potentials with Mixtures of Experts
par: Liu, Yuzhi, et autres
Publié: (2026)

Adaptive Loss Weighting for Machine Learning Interatomic Potentials
par: Ocampo, Daniel, et autres
Publié: (2024)

A "Magnetic" Machine Learning Interatomic Potential for Nickel
par: Gong, Xiaoguo, et autres
Publié: (2023)

Interface Design Beyond Epitaxy: Oxide Heterostructures Comprising Symmetry-forbidden Interfaces
par: Wang, Hongguang, et autres
Publié: (2024)

Laser-driven ferroelectricity in $\mathrm{SrTiO_{3}}$ via quantum fluctuation quenching
par: Libbi, Francesco, et autres
Publié: (2025)

Bias in Universal Machine-Learned Interatomic Potentials and its Effects on Fine-Tuning
par: Wong, Nicolas, et autres
Publié: (2026)