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arXiv Machine Learning

Reactivity-Informed Machine Learning for Performance Prediction and Design Space Exploration of Alkali-Activated Slag

arXiv:2606. 06765v1 Announce Type: cross Abstract: Establishing quantitative relationships among mix design, raw material properties, curing conditions, and performance remains a long-standing challenge in cementitious materials, particularly for alkali-activated materials with variable precursor and activator chemistry.

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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 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
Jul 27

Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives

arXiv:2607. 21660v1 Announce Type: cross Abstract: Artificial intelligence (AI) is accelerating materials prediction and design by enabling efficient exploration of chemical and structural spaces, with particular promise for novel materials discovery.

By Xianyuan Liu, Charles Anjah, Benjamin E. Jolly, Jonathon F. S. Markanday, Joshua Berry, Haolin Wang, Nicola A. Morley, Robert D. J. Oliver, Alexandra J. Ramadan, Delvin Ce Zhang, Katerina A. Christofidou, Haiping Lu
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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
fine-tuningefficiencybenchmarks
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arXiv Machine Learning
Jun 2

Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design

arXiv:2606. 02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under physical constraints.

By Anand Babu, Rog\'erio Almeida Gouv\^ea, Gian-Marco Rignanese
diffusionreinforcement-learningmultimodal
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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
llmsdiffusionreinforcement-learning
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arXiv AI
1d ago

Data-knowledge dual-driven intelligent framework for full-chain, experiment-efficient synthesis of 2D dendrites

arXiv:2603. 16959v2 Announce Type: replace-cross Abstract: Exemplified by the chemical vapor deposition growth of two-dimensional dendrites, which has potential applications in catalysis and presents a parameter-intensive, data-scarce and reaction process-complex model problem, we devise a machine intelligence-empowered framework for the full chain support of material synthesis, encompassing rapid process optimization, accurate customized synthesis, and comprehensive mechanism deciphering.

By Wenqiang Huang, Xuhang Gu, Susu Fang, Shen'ao Xue, Huanhuan Xing, Junjie Jiang, Junying Zhang, Shen Zhou, Zheng Luo, Jin Zhang, Fangping Ouyang, Shanshan Wang
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