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.
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.
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.
AutoXRD is an autonomous large language model (LLM) agent framework designed to automate powder X-ray diffraction (XRD) analysis by structuring the process as stepwise refinement, grounding actions in observed evidence, and applying deterministic crystallographic and physical checks. The authors introduce XRDBench, comprising two tracks: XRDBench-QA with 100 diagnostic tasks focused on scientific reasoning, and XRDBench-E2E with 34 executable workflows that test full analysis capabilities, including file inspection, software execution, iterative refinement, evidence preservation, and reporting. Evaluation of ten recent LLMs on 1,340 model–task runs shows average scores of 57.8, with GPT‑5.6 Sol achieving the highest overall score of 81.1; the study also identifies key failure modes such as coupled‑parameter control and quantitative reasoning, highlighting areas for future improvement.
arXiv:2510.19251v2 Announce Type: replace-cross Abstract: Predicting which hypothetical inorganic crystals can be experimentally realized remains a central challenge in accelerating materials discove...
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.
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: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.
arXiv:2606. 00938v1 Announce Type: cross Abstract: Data-driven surrogate models are an alternative to numerical homogenization of heterogeneous materials.
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.
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.
arXiv:2607. 28695v1 Announce Type: cross Abstract: Here is the plain text version optimized for arXiv's submission form.
uFlowCSP is a MeanFlow-based crystal structure prediction model that learns the average probability‑flow velocity, enabling it to generate complete crystal structures in one to five network evaluations. It achieves inference speeds 5×–58× faster than diffusion and flow‑matching methods while matching or surpassing their performance, with a chemistry‑ and symmetry‑aware Transformer that uses canonical atom ordering and per‑token chemistry embeddings. On the MP‑20 benchmark, uFlowCSP attains comparable or higher accuracy with far fewer evaluations and significantly lower wall‑clock time, demonstrating improved accuracy per network evaluation.