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arXiv Machine Learning

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

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.

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arXiv Machine Learning
Sep 21

Assessment of Machine Learning-Based Critical Heat Flux Models in the CTF Subchannel Code for Square Rod Bundle Prediction

The paper evaluates machine learning (ML) models for predicting critical heat flux (CHF) in square rod bundles using the CTF subchannel code and the EPRI rod bundle CHF database. It compares pure and hybrid residual correction models in local and semilocal forms, finding that tube-trained ML models transfer well to rod bundle geometries and outperform traditional empirical correlations and lookup tables. The local hybrid LUT model shows the best overall performance, while the semilocal pure ML model remains highly competitive, indicating that significant improvements in rod bundle CHF prediction are achievable even with tube-only training data.

By Aidan Furlong, Vinicius de Melo Monteiro, Robert Salko, Juliana Pacheco Duarte, Xu Wu
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arXiv Machine Learning
5d ago

Retrainable physics-integrated neural differentiable modeling of sintering across material systems

Sinter-PiNDiff is a retrainable physics‑integrated neural differentiable framework that predicts density and grain‑size evolution during sintering. It uses two neural networks to learn densification and grain‑growth coefficients within coupled rate equations, and a smooth saturation factor to limit densification near theoretical density. When trained on published data for MgO, Al‑doped ZnO, and CaO‑doped ThO₂, the model achieved the lowest mean errors across twelve material‑metric comparisons compared to multilayer perceptron and residual network baselines, demonstrating the importance of density‑dependent kinetic feedback and providing uncertainty estimates via deep ensembles.

By Zeping Chen, Ani Aprahamian, Khachatur V. Manukyan, Tengfei Luo
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arXiv Machine Learning
Sep 17

Physics-based prediction, uncertainty quantification and decision-making for IN718 crystallographic texture intensity across LPBF defocus regimes

This study introduces a two‑stage physics‑based model to predict the build‑direction crystallographic texture intensity of Inconel 718 produced by laser powder bed fusion. Stage 1 maps process parameters to melting mode and melt‑pool geometry, while Stage 2 combines an empirical physics model with a random‑forest residual correction, attenuated by k‑nearest‑neighbor weighting and a beam‑power‑density gate. The physics‑anchored approach achieves substantially higher predictive accuracy (R² ≈ 0.78) than black‑box models and provides calibrated uncertainty estimates that allow selective withholding of predictions outside the model’s valid domain.

By Yisheng Lu, John Riris, Jie Song, Yao Fu, Jie Chen
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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
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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
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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)
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arXiv Machine Learning
Aug 17

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
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