Cover Image

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Miret, Santiago, Lee, Kin Long Kelvin, Gonzales, Carmelo, Mannan, Sajid, Krishnan, N. M. Anoop
Format:	Preprint
Published:	2025
Subjects:	Materials Science Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Evaluating Universal Machine Learning Force Fields Against Experimental Measurements
by: Mannan, Sajid, et al.
Published: (2025)

Are LLMs Ready for Real-World Materials Discovery?
by: Miret, Santiago, et al.
Published: (2024)

Deconstructing equivariant representations in molecular systems
by: Lee, Kin Long Kelvin, et al.
Published: (2024)

Navigating energy landscapes for materials discovery: Integrating modeling, simulation, and machine learning
by: Sajid Mannan, et al.
Published: (2024)

Natural Language Embeddings of Synthesis and Testing conditions Enhance Glass Dissolution Prediction
by: Mannan, Sajid, et al.
Published: (2026)

Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
by: Kim, Jaesun, et al.
Published: (2025)

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

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

Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials
by: Nong, Wei, et al.
Published: (2025)

Sustainable Materials Discovery in the Era of Artificial Intelligence
by: Mannan, Sajid, et al.
Published: (2026)

Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems
by: Tahmasbi, Hossein, 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)

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

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications
by: In Kee Park, et al.
Published: (2026)

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors
by: Du, Hongwei, et al.
Published: (2025)

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

A Simple and Efficient Non-DFT-Based Machine Learning Interatomic Potential to Simulate Titanium MXenes
by: Thomazini, Luis F. V., et al.
Published: (2025)

Hierarchical Crystal Structure Prediction of Zeolitic Imidazolate Frameworks Using DFT and Machine-Learned Interatomic Potentials
by: Xu, Yizhi, et al.
Published: (2026)

Towards Accurate and Scalable High-throughput MOF Adsorption Screening: Merging Classical Force Fields and Universal Machine Learned Interatomic Potentials
by: Bonakala, Satyanarayana, 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)

Benchmarking Universal Machine Learning Interatomic Potentials for Supported Nanoparticles: Decoupling Energy Accuracy from Structural Exploration
by: Xu, Jiayan, et al.
Published: (2025)

Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials
by: Hattori, Shinnosuke, et al.
Published: (2025)

Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning
by: Varughese, Bilvin, et al.
Published: (2025)

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

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

A "Magnetic" Machine Learning Interatomic Potential for Nickel
by: Gong, Xiaoguo, et al.
Published: (2023)

Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential
by: Sivak, Jacob T., et al.
Published: (2024)

Surface Stability Modeling with Universal Machine Learning Interatomic Potentials: A Comprehensive Cleavage Energy Benchmarking Study
by: Mehdizadeh, Ardavan, et al.
Published: (2025)

Uni2D: A Universal Machine Learning Interatomic Potential for Two-Dimensional Materials
by: Wang, Haidi, et al.
Published: (2025)

Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
by: Kuroda, Naoya, et al.
Published: (2026)

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

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

Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity
by: Hu, Yanxiao, 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)

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

Phosphorus-based lubricant additives on iron with Machine Learning Interatomic Potentials
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)

Machine Learned Interatomic Potentials for Ternary Carbides trained on the AFLOW Database
by: Roberts, Josiah, et al.
Published: (2024)

Evaluation of Foundational Machine Learned Interatomic Potentials for Migration Barrier Predictions
by: Bheemaguli, Achinthya Krishna, et al.
Published: (2025)

Machine Learning Interatomic Potentials for Million-Atom Simulations of Multicomponent Alloys
by: Shuang, Fei, et al.
Published: (2026)