arXiv Machine Learning July 14, 2026 By Yugandhar Kasala Sreenivasulu, Isshu Lee, John W. Merickel, Fei Xu, Yalei Tang, Joshua E. Rittenhouse, Aleksandar Vakanski, Rongjie Song

Machine Learning-based Correlation of Charpy Impact Properties Between Sub-sized and Standard-sized Specimens for Nuclear Structural Materials

Read the original on arXiv Machine Learning →

arXiv:2607. 10412v1 Announce Type: new Abstract: Reliable correlations of Charpy impact test results between sub-sized and full-sized specimens are essential for structural integrity assessments, particularly in nuclear applications, where spatial constraints and limited material volume restrict specimen size.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 8

Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Constrained Neural Network

arXiv:2604. 27775v2 Announce Type: replace-cross Abstract: Shallow nanoindentation enables mechanical characterization of thin films, individual phases, and other volume-constrained materials, but the measured hardness is inflated by the indentation size effect (ISE).

By Radmir Karamov, Tagir Karamov

benchmarks

arXiv Machine Learning

Jun 2

Machine Learning Surrogate Modeling for Homogenization of Hyperelastic Materials with Boolean Microstructures

arXiv:2606. 00938v1 Announce Type: cross Abstract: Data-driven surrogate models are an alternative to numerical homogenization of heterogeneous materials.

By Matthias Br\"andel, Oliver Rheinbach

arXiv Machine Learning

Aug 11

Distribution-Free Conformal Prediction for Steel Fatigue Strength: Marginal Validity Is Not Enough

arXiv:2608. 07589v1 Announce Type: cross Abstract: Predicting fatigue failure in steel components experimentally is costly because it requires testing across multiple compositions and processing conditions.

By Irene Boruah

safety

arXiv Machine Learning

Jul 23

Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework

arXiv:2604. 14562v2 Announce Type: replace Abstract: Accurate temperature field prediction in metal additive manufacturing (AM) is essential for understanding the process-structure-performance relationship.

By Hyeonsu Lee, Jihoon Jeong

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 18

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.

By Youngwoo Cho, Seunghoon Yi, Wooil Yang, Sungmo Kang, Young-woo Son, Jaegul Choo, Joonseok Lee, Soo Kyung Kim, Hongkee Yoon

fine-tuning efficiency benchmarks

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Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Constrained Neural Network

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