arXiv Machine Learning

From fragmented data to actionable design: Physics-calibrated learning for plastic upcycling

arXiv:2608. 02402v1 Announce Type: new Abstract: Thermochemical upgrading of plastic waste is a key upcycling pathway, yet the experimental literature is fragmented by heterogeneous conditions and incomplete reporting.

arXiv Machine Learning

Jun 9

GPT-Micro: A large language paradigm for accelerated, inexpensive, and thermodynamics-consistent discovery of constitutive models in manufacturing

arXiv:2606. 08238v1 Announce Type: new Abstract: Constitutive modeling of the relationship between process-imposed material states and fundamental material properties is critical to control of material microstructure in manufacturing processes.

By Soumik Dutta, Kiarash Naghavi Khanghah, Sania Shree, Logan McNeil, Thomas Feldhausen, Hongyi Xu, Rajiv Malhotra

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arXiv AI

Jun 16

Sustainable Materials Discovery in the Era of Artificial Intelligence

arXiv:2601. 21527v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening.

By Sajid Mannan, Rupert J. Myers, Rohit Batra, Rocio Mercado, Lothar Wondraczek, N. M. Anoop Krishnan

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

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arXiv Machine Learning

Aug 6

DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery

arXiv:2608. 05120v1 Announce Type: new Abstract: Kinetic model discovery is a central challenge in chemical engineering, as accurate rate expressions are essential for understanding and controlling chemical and biological processes.

By Roberto Aliaga Medina, Paulina Quintanilla, Antonio del Rio Chanona

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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)

arXiv Machine Learning

Jul 10

Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling

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.

By Sarah Grewe, J\"org Frochte

Hugging Face Trending Papers

Aug 5

DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery

Kinetic model discovery is a central challenge in chemical engineering, as accurate rate expressions are essential for understanding and controlling chemical and biological processes. Symbolic regression (SR) has emerged as a powerful data-driven approach for identifying interpretable kinetic models, but usually operates without domain knowledge, often exploring physicochemically implausible models.

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arXiv AI

Jul 28

Cost-Aware Recovery-Pathway Identification and Bayesian Optimization for Autonomous Materials Discovery

arXiv:2607. 23896v1 Announce Type: new Abstract: Autonomous laboratories automate experimental execution, but a campaign must also decide which recovery pathway merits optimization.

By Debajyoti Ray, Niranjan Srinivas

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arXiv AI

Jul 3

Grounded autonomous research: a fault-tolerant LLM pipeline from corpus to manuscript in frontier computational physics

arXiv:2607. 02329v1 Announce Type: new Abstract: Autonomous-research agents have demonstrated end-to-end LLM automation in machine-learning sandboxes where execution provides calibration.

By Haonan Huang

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arXiv AI

Jul 23

MOF-Sleuth: Tool-Grounded Reward Alignment for Explainable Fine-Grained MOF CIF Auditing

arXiv:2607. 19935v1 Announce Type: new Abstract: Large metal-organic framework (MOF) databases support simulation, screening, and machine learning through crystallographic information files (CIFs).

By Yu Liu, Zhiwei Yang, Diandian Guo, Kun Peng, Fangfang Yuan, Cong Cao, Chaozhuo Li, Zhiyuan Ma, Yanbing Liu, Guobin Zhao

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arXiv Machine Learning

Jun 9

Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules

arXiv:2606. 08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.

By Riccardo De Santi, Bruce Lee, Cristian Perez Jensen, Kimon Protopapas, Sophia Tang, Cheng-Hao Liu, Pranam Chatterjee, Yisong Yue, Andreas Krause

diffusion

Hugging Face Trending Papers

Aug 10

Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models

Predicting the answer to interventional ``what if'' questions --- the outcome of an action never taken --- requires a \emph{mechanistic}, causal model, not a curve fit; and learning such a model requires \emph{experiments}, because passive data leaves its mechanisms unidentified. Experiments are expensive, so the central problem is \emph{data efficiency}.

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