Cover Image

MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chiang, Yuan, Kreiman, Tobias, Zhang, Christine, Kuner, Matthew C., Weaver, Elizabeth, Amin, Ishan, Park, Hyunsoo, Lim, Yunsung, Kim, Jihan, Chrzan, Daryl, Walsh, Aron, Blau, Samuel M., Asta, Mark, Krishnapriyan, Aditi S.
Format:	Preprint
Published:	2025
Subjects:	Chemical Physics Materials Science Computational Engineering, Finance, and Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
by: Liu, Ryan, et al.
Published: (2026)

Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields
by: Kreiman, Tobias, et al.
Published: (2025)

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
by: Amin, Ishan, et al.
Published: (2025)

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
by: Kuner, Matthew C., et al.
Published: (2025)

Bridging the Simulation-to-Experiment Gap with Generative Models using Adversarial Distribution Alignment
by: Nelson, Kai, et al.
Published: (2026)

Transformers Discover Molecular Structure Without Graph Priors
by: Kreiman, Tobias, et al.
Published: (2025)

Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
by: Raja, Sanjeev, et al.
Published: (2024)

DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
by: Han, Kevin, et al.
Published: (2025)

Crystalyse: a multi-tool agent for materials design
by: Nduma, Ryan, et al.
Published: (2025)

MLIP-MC: A Framework for Adsorption Simulations using Machine-Learned Interatomic Potentials
by: Edwards, Connor W., et al.
Published: (2026)

Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects
by: Wang, Xinwei, et al.
Published: (2026)

Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
by: Raja, Sanjeev, et al.
Published: (2025)

Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows
by: Li, Wenwen, et al.
Published: (2026)

DFT and MLIP study of solute segregation to coherent and semi-coherent α-Fe/Fe$_3$C interfaces
by: Reiners-Sakic, Amin, et al.
Published: (2026)

Bayesian Selection for Efficient MLIP Dataset Selection
by: Rocke, Thomas, et al.
Published: (2025)

Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
by: Luo, Shengjie, et al.
Published: (2024)

Continuous SUN (Stable, Unique, and Novel) Metric for Generative Modeling of Inorganic Crystals
by: Negishi, Masahiro, et al.
Published: (2025)

The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
by: Qu, Eric, et al.
Published: (2024)

Na Vacancy Driven Phase Transformation and Fast Ion Conduction in W-doped Na$_3$SbS$_4$ from Machine Learning Force Fields
by: Klarbring, Johan, et al.
Published: (2024)

Platonic representation of foundation machine learning interatomic potentials
by: Li, Zhenzhu, et al.
Published: (2025)

A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention
by: Qu, Eric, et al.
Published: (2026)

Dynamic Vacancy Levels in CsPbCl3 Obey Equilibrium Defect Thermodynamics
by: Mosquera-Lois, Irea, et al.
Published: (2025)

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

Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields
by: Liang, Xia, et al.
Published: (2025)

Sulfur Vacancies Limit the Open-circuit Voltage of Sb2S3 Solar Cells
by: Wang, Xinwei, et al.
Published: (2024)

Illuminating the property space in crystal structure prediction using Quality-Diversity algorithms
by: Wolinska, Marta, et al.
Published: (2024)

Nanoscale Mapping of the Subcellular Glycosylation Landscape
by: Helene Gregoria Schroeter, et al.
Published: (2025)

Fair Societies: Algorithms for House Allocations
by: Hosseini, Hadi, et al.
Published: (2025)

Point defect formation at finite temperatures with machine learning force fields
by: Mosquera-Lois, Irea, et al.
Published: (2024)

Interatomic potentials for platinum
by: Koju, R. K., et al.
Published: (2026)

Graph-Network-Based Predictive Modeling for Highly Cross-Linked Polymer Systems
by: Lee, Wonseok, et al.
Published: (2023)

Exact Maximin Share Fairness via Adjusted Supply
by: Barman, Siddharth, et al.
Published: (2025)

Guiding Generative Models to Uncover Diverse and Novel Crystals via Reinforcement Learning
by: Park, Hyunsoo, et al.
Published: (2025)

Upper efficiency limit of Sb2Se3 solar cells
by: Wang, Xinwei, et al.
Published: (2024)

Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost
by: Baghishov, Ilgar, et al.
Published: (2025)

Fair and Efficient Allocation of Indivisible Mixed Manna
by: Barman, Siddharth, et al.
Published: (2025)

Mixed Fair Division: A Survey
by: Liu, Shengxin, et al.
Published: (2023)

Fair Lotteries for Participatory Budgeting
by: Aziz, Haris, et al.
Published: (2024)

Four-electron Negative-U Vacancy Defects in Antimony Selenide
by: Wang, Xinwei, et al.
Published: (2023)

LitMOF: An LLM Multi-Agent for Literature-Validated Metal-Organic Frameworks Database Correction and Expansion
by: Kim, Honghui, et al.
Published: (2025)