arXiv Machine Learning

Decision-Aware Evaluation of Physics-Informed Surrogates

arXiv:2606. 07146v1 Announce Type: new Abstract: Physics-informed machine learning is often assessed by curve error, although engineering use depends on downstream decisions: ranking candidates, avoiding infeasible designs and limiting regret.

arXiv AI

Jul 1

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design

arXiv:2606. 28578v1 Announce Type: cross Abstract: Closed-loop materials discovery iterates between proposing candidate structures and evaluating their properties, and property evaluation dominates the cost.

By Sk Md Ahnaf Akif Alvi, Jan Janssen, Danny Perez, Douglas Allaire, Raymundo Arroyave

rag diffusion reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Aug 3

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery

arXiv:2607. 29225v1 Announce Type: new Abstract: Bayesian Optimization (BO) is widely adopted for data-efficient optimization in scientific and engineering applications, yet its computational cost is rarely evaluated alongside optimization performance.

By Panagiotis Krokidas, Christoforos Rekatsinas, Vassilis Sioros, Grigorios M. Chatziathanasiou, Efi-Maria Papia, George Giannakopoulos

robotics benchmarks

arXiv Machine Learning

Aug 4

Surrogate Modeling for the Design of Optimal Lattice Structures using Tensor Completion

arXiv:2510. 07474v2 Announce Type: replace Abstract: When designing new materials, it is often necessary to design a material with specific desired properties.

By Shaan Pakala, Aldair E. Gongora, Brian Giera, Evangelos E. Papalexakis

arXiv Machine Learning

Jul 16

Microstructure-Conditioned Surrogate Models for Graded Multiscale Optimization of Mycelium Composites

arXiv:2607. 13688v1 Announce Type: new Abstract: Emerging sustainable materials increasingly rely on engineered hierarchy and microstructure to achieve control of their properties and mechanical behavior.

By J. Storm, I. B. C. M. Rocha, S. Schyck, K. Masania, F. P. van der Meer

arXiv Machine Learning

Aug 4

PolymerGPT: Multi-property Optimization with a Decoder-Based GPT Model for Generative Polymer Design

arXiv:2608. 01431v1 Announce Type: cross Abstract: Polymer property prediction and inverse generative design targeting desired properties are two crucial tasks in machine learning-assisted polymer design.

By Charlie Pyle, Adarsh Gadari, C. Adrian Figg, Zhenquan Jia, Yaohang Li, Chunjiang Zhu

llms

arXiv Machine Learning

Jul 10

Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling

arXiv:2607. 07863v1 Announce Type: new Abstract: In physically dominated machining processes, experimental datasets are small, expensive, and material-specific; in this regime, data curation, evaluation design, and the form of physics integration can matter as much as the learning algorithm.

By Sarah Grewe, J\"org Frochte

Hugging Face Trending Papers

Aug 2

PolymerGPT: Multi-property Optimization with a Decoder-Based GPT Model for Generative Polymer Design

Polymer property prediction and inverse generative design targeting desired properties are two crucial tasks in machine learning-assisted polymer design. While the former has received considerable attention, there have been limited methods developed for the latter.

llms

arXiv Machine Learning

Jun 11

Tensor Methods: A Unified and Interpretable Approach for Material Design

arXiv:2602. 10392v2 Announce Type: replace Abstract: When designing new materials, it is often necessary to tailor the material design to have some desired properties.

By Shaan Pakala, Aldair E. Gongora, Brian Giera, Evangelos E. Papalexakis

safety

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 AI

2d ago

Benchmarking data-driven material models on the classic Treloar dataset

arXiv:2608. 14063v1 Announce Type: new Abstract: Machine learning is rapidly reshaping constitutive modeling, offers new ways to learn material behavior directly from experimental data, and challenges long-established modeling paradigms.

By Hagen Holthusen, Moritz Flaschel, Denisa Martonov\'a, Ellen Kuhl

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 AI

Jul 9

Physics-Audited Agentic Discovery in Scientific Machine Learning

arXiv:2607. 07379v1 Announce Type: new Abstract: In agentic scientific machine learning (SciML), large language model (LLM) agents can discover surrogate models and select one by an automated score, typically an error metric.

By Diab W. Abueidda, Bilal Ahmed, Panos Pantidis, Mostafa E. Mobasher

llms agents

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