arXiv Machine Learning

Robust and Interpretable Adaptation of Equivariant Materials Foundation Models via Sparsity-promoting Fine-tuning

arXiv:2606. 18691v1 Announce Type: new Abstract: Pre-trained materials foundation models, or machine learning interatomic potentials, leverage general physicochemical knowledge to effectively approximate potential energy surfaces.

arXiv Machine Learning

Jun 16

Distilling latent electrostatics from foundation machine learning interatomic potentials

arXiv:2606. 15001v1 Announce Type: cross Abstract: Foundation machine learning interatomic potentials (MLIPs) have enabled atomistic simulations across broad regions of chemical and materials space, but many remain computationally expensive and lack explicit electrostatics, limiting their use for systems governed by long-range interactions and electrical response.

By Xiaoyu Wang, Bingqing Cheng

fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 19

Evaluating Universal Machine Learning Force Fields Against Experimental Measurements

arXiv:2508. 05762v2 Announce Type: replace-cross Abstract: Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodic table.

By Sajid Mannan, Vaibhav Bihani, Carmelo Gonzales, Kin Long Kelvin Lee, Nitya Nand Gosvami, Sayan Ranu, Santiago Miret, N M Anoop Krishnan

benchmarks

arXiv Machine Learning

Jun 9

Inverse design of bespoke interatomic potentials via active learning by information-matching

arXiv:2606. 08148v1 Announce Type: cross Abstract: Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selection of training data, quantified uncertainty, and model expressiveness.

By Yonatan Kurniawan (Department of Physics and Astronomy, Brigham Young University, Provo, UT, USA), Logan D. Williams (Lawrence Livermore National Laboratory, Livermore, CA, USA), Amit Samanta (Lawrence Livermore National Laboratory, Livermore, CA, USA), Ilia Nikiforov (Department of Aerospace Engineering and Mechanics, University of Minnesota, Minneapolis, MN, USA), Daniel Schwalbe-Koda (Department of Materials Science and Engineering, University of California, Los Angeles, CA, USA), Mark K. Transtrum (Cross Stream Consulting, Springville, UT, USA), Ellad B. Tadmor (Department of Aerospace Engineering and Mechanics, University of Minnesota, Minneapolis, MN, USA), Vincenzo Lordi (Lawrence Livermore National Laboratory, Livermore, CA, USA), Vasily V. Bulatov (Lawrence Livermore National Laboratory, Livermore, CA, USA)

arXiv Machine Learning

Jul 17

Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields

arXiv:2607. 14486v1 Announce Type: cross Abstract: Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning workflows.

By Sheng Bi, Yi-Ze Wang, Jun Cheng

fine-tuning

arXiv Machine Learning

Jun 2

Benchmark Dataset for Catalysis on 2D MXenes

arXiv:2606. 00794v1 Announce Type: cross Abstract: Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials.

By Pavlo Melnyk, Anmar Karmush, M{\aa}rten Wadenb\"ack, Ania Beatriz Rodr\'iguez-Barrera, Johanna Rosen, Michael Felsberg, Jonas Bj\"ork

benchmarks

arXiv Machine Learning

2d ago

SPEAR: Structure Property Explainability with Attention Regularization

arXiv:2608. 13826v1 Announce Type: cross Abstract: Machine learning is increasingly used to learn structure property relationships from spectroscopic and diffraction data, yet its adoption in materials discovery is often limited by poor interpretability of model predictions.

By Aditya Raghavan, Utkarsh Pratiush, Dalton A. Pearl, Jade Holliman Jr, Katharine Page, Philip D Rack, Sergei V Kalinin

benchmarks safety

arXiv Machine Learning

Jul 28

MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals

arXiv:2604. 13897v2 Announce Type: replace Abstract: We present an open Molecular Crystal (MC) database of Machine-Learned Interatomic Potentials (MLIP) called MolCryst-MLIPs.

By Adam Lahouari, Shen Ai, Jihye Han, Jillian Hoffstadt, Philipp Hoellmer, Charlotte Infante, Pulkita Jain, Sangram Kadam, Maya M. Martirossyan, Amara McCune, Hypatia Newton, Shlok J. Paul, Willmor Pena, Jonathan Raghoonanan, Sumon Sahu, Oliver Tan, Andrea Vergara, Jutta Rogal, Mark E. Tuckerman

fine-tuning benchmarks

arXiv Machine Learning

Jun 10

Gradient-Guided Furthest Point Sampling for Robust Training Set Selection

arXiv:2510. 08906v2 Announce Type: replace-cross Abstract: Training set sampling methods are used to improve model performance and lower data costs in machine learning problems relevant to chemistry.

By Morris Trestman, Stefan Gugler, Felix A. Faber, O. A. von Lilienfeld

arXiv Machine Learning

Jun 2

Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design

arXiv:2606. 02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under physical constraints.

By Anand Babu, Rog\'erio Almeida Gouv\^ea, Gian-Marco Rignanese

diffusion reinforcement-learning multimodal

arXiv AI

Jun 2

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures

arXiv:2602. 04861v2 Announce Type: replace-cross Abstract: Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can miss.

By Ryan Liu, Eric Qu, Tobias Kreiman, Samuel M. Blau, Aditi S. Krishnapriyan

llms benchmarks

arXiv AI

Jul 9

Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning

arXiv:2607. 07708v1 Announce Type: cross Abstract: Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization.

By Chen Tang, Yizhou Wang, Jianyu Wu, Lintao Wang, Shixiang Tang, Pengze Li, Encheng Su, Jun Yao, Jiabei Xiao, Yuqi Shi, Jielan Li, Hongxia Hao, Zhangyang Gao, Fang Wu, Ben Fei, Xiangyu Yue, Pan Tan, Bozitao Zhong, Jinouwen Zhang, Aoran Wang, Yan Lu, Jiaheng Liu, Xinzhu Ma, Liang Hong, Mingyue Zheng, Phil Torr, Bowen Zhou, Wanli Ouyang, Lei Bai

llms multimodal benchmarks

arXiv Machine Learning

Jun 2

PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials

arXiv:2601. 07742v4 Announce Type: replace-cross Abstract: Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on energy, force, and stress errors can exhibit error in curvature, degrading the prediction of vibrational properties.

By Teddy Koker, Abhijeet Gangan, Mit Kotak, Jaime Marian, Tess Smidt

fine-tuning benchmarks

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