Imagen de Portada

Data for the publication "Parameter-Efficient Fine-Tuning of Machine-Learning Interatomic Potentials for Phonon and Thermal Properties"

Fuente: Zenodo

Guardado en:

Detalles Bibliográficos
Autores principales:	Grandel, Jonas, Benner, Philipp, George, Janine
Formato:	Recurso digital
Lenguaje:	inglés
Publicado:	Zenodo 2026
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Parameter-Efficient Fine-Tuning of Machine-Learning Interatomic Potentials for Phonon and Thermal Properties
por: Grandel, Jonas, et al.
Publicado: (2026)

PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
por: Koker, Teddy, et al.
Publicado: (2026)

Universal Machine Learning Interatomic Potentials are Ready for Phonons
por: Loew, Antoine, et al.
Publicado: (2024)

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

Bias in Universal Machine-Learned Interatomic Potentials and its Effects on Fine-Tuning
por: Wong, Nicolas, et al.
Publicado: (2026)

Target-Distribution-Guided Cross-Functional Fine-Tuning of Machine-Learning Interatomic Potentials
por: Nagai, Yuki, et al.
Publicado: (2026)

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
por: Liu, Xiaoqing, et al.
Publicado: (2025)

SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction
por: Amariamir, Sasan, et al.
Publicado: (2024)

A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs)
por: Liu, Xiaoqing, et al.
Publicado: (2025)

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

Machine Learning a Universal Harmonic Interatomic Potential for Predicting Phonons in Crystalline Solids
por: Lee, Huiju, et al.
Publicado: (2024)

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
por: Jonas Böhm, et al.
Publicado: (2026)

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
por: Böhm, Jonas, et al.
Publicado: (2025)

Efficient and Accurate Machine Learning Interatomic Potential for Graphene: Capturing Stress-Strain and Vibrational Properties
por: Hawthorne, Felipe, et al.
Publicado: (2025)

Transport Novelty Distance: A Distributional Metric for Evaluating Material Generative Models
por: Hagemann, Paul, et al.
Publicado: (2025)

Benchmarking Universal Machine Learning Interatomic Potentials for Elastic Property Prediction
por: Gao, Pengfei, et al.
Publicado: (2025)

Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials
por: Zhou, Yuanchang, et al.
Publicado: (2026)

Phonon and Thermal Properties of Silicon Carbide: A Comparison of Empirical and Machine Learning Potentials
por: Jian Zhang, et al.
Publicado: (2024)

Machine Learning Interatomic Potential for Alumina
por: Galib, Musanna
Publicado: (2025)

Ligand-Controlled Phonon Dynamics in CsPbBr3 Nanocrystals Revealed by Machine-Learned Interatomic Potentials
por: Cha, Seungjun, et al.
Publicado: (2026)

Machine Learning Interatomic Potentials with Keras API
por: Rili, James Paolo
Publicado: (2024)

Machine Learning Interatomic Potentials for Heterogeneous Catalysis
por: Deqi Tang, et al.
Publicado: (2024)

Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes
por: Zou, Joanna, et al.
Publicado: (2026)

MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials
por: Zhou, Yuanchang, et al.
Publicado: (2026)

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

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials
por: Wang, Hongyu, et al.
Publicado: (2026)

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

Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
por: Birks, Fraser, et al.
Publicado: (2025)

Scalable Data-Driven Basis Selection for Linear Machine Learning Interatomic Potentials
por: Torabi, Tina, et al.
Publicado: (2025)

Smooth Dynamic Cutoffs for Machine Learning Interatomic Potentials
por: Han, Kevin, et al.
Publicado: (2026)

Importance of Electronic Entropy for Machine Learning Interatomic Potentials
por: Petersen, Martin Hoffmann, et al.
Publicado: (2026)

Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials
por: Nourollah, Amir Masoud, et al.
Publicado: (2026)

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

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

Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials
por: Perez, Danny, et al.
Publicado: (2025)

Classical and Machine Learning Interatomic Potentials for BCC Vanadium
por: Wang, Rui, et al.
Publicado: (2022)

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

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

Machine Learning Interatomic Potential‐Enabled Discovery of Chlorofullerenes
por: Zi‐Yang Qiu, et al.
Publicado: (2025)

Adaptive Loss Weighting for Machine Learning Interatomic Potentials
por: Ocampo, Daniel, et al.
Publicado: (2024)