arXiv AI By Mahish K. Guru, Jan Bohlen, Louam Lemjid, Marius Tacke, Roland Aydin, Noomane Ben Khalifa

Which alloy composition,what process parameters? Inferring the recipe from optimized metallic microstructure and texture

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arXiv Machine Learning
Sep 2

Generative artificial intelligence for reliable mechanistic reasoning for corrosion

The paper introduces a retrieval‑augmented generation framework that combines fine‑tuned open‑weight language models with a hybrid dense‑lexical retrieval pipeline to synthesize corrosion knowledge. Applied to magnesium alloy corrosion, the system achieves significant improvements in token‑level accuracy and high faithfulness and context recall, while an additional Reason Map framework constructs evidence graphs to detect causal errors that standard factuality metrics miss. The modular design is positioned as a general blueprint for trustworthy AI‑assisted knowledge synthesis across engineering domains.

By Bharath M N, R K Singh Raman, Alankar Alankar
arXiv AI
Sep 30

Co-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Design

Co-PiLOT is a latent optimization framework that maps candidate physical structures through a generative encoder-decoder, using the decoder as a learned validity prior and performing physics-informed black-box optimization in latent space. It is applied to the inverse design of magnesium alloy microstructure/texture, employing a vision transformer encoder paired with latent diffusion, diffusion transformer, and rectified-flow transformer decoders trained on an 80,000-sample EBSD dataset to produce a minimal bottleneck representation. The MERIDIAN optimizer, driven by a deep-kernel Gaussian process and failure-aware feasibility prediction, achieves the best target-driven objective score within 160 simulations, reducing relative target error by 3–22% compared to seven baseline methods.

By Mahish K. Guru, Mayank Nagar, Ayush vyas, Jan Bohlen, Roland Aydin, Noomane Ben Khalifa
arXiv AI
Jun 9

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science

arXiv:2606. 07712v1 Announce Type: cross Abstract: Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural networks for property prediction, diffusion and flow-matching models for crystal generation -- each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems.

By Zhan'ao Yao, Boxuan Zhang, Jingyuan Shu, Xiaoyu Wu, Rongyan Wang, Linjing Li, Dajun Zeng, Yudong Yao, Tingwei Chen, Youwei Wang, Xiaolin Zhao, Jiahui Shi, Jianjun Liu