arXiv Machine Learning August 4, 2026 By Jingyang Bai, Zijia Wang, Xiangyi Long, Marcos Millan, Binjian Nie, Mingyue Ding

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

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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

llms agents benchmarks safety

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 AI

Sep 11

Decision-Focused Active Learning for Scale-Aware Critical-Materials Recovery

The paper presents a decision‑focused active learning framework for optimizing scale‑aware recovery of critical materials, using data from the Pacific Northwest National Laboratory’s CICERO workflow on autonomous selective precipitation. By adaptively selecting experiments based on prior results, the method achieves the best recorded enrichment of recycled neodymium‑iron‑boron magnets with roughly half the number of experiments compared to non‑adaptive approaches, and demonstrates similar efficiency for samarium‑cobalt magnets. The authors propose a Bayesian risk‑based batch selection strategy and outline a prospective test plan with standardized logging and economic validation.

By Niranjan Srinivas, Debajyoti Ray, Elias Nakouzi

agents benchmarks

Hugging Face Trending Papers

Sep 8

Decision-Focused Active Learning for Scale-Aware Critical-Materials Recovery

Choosing a recovery process for scale-up requires connecting laboratory results with product requirements, process costs, and scale effects. We analyze records from Pacific Northwest National Laborato...

agents benchmarks

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

diffusion reinforcement-learning multimodal

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

llms rag benchmarks

One email a morning, machine-written

Related stories

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

Sustainable Materials Discovery in the Era of Artificial Intelligence

Decision-Focused Active Learning for Scale-Aware Critical-Materials Recovery

Decision-Focused Active Learning for Scale-Aware Critical-Materials Recovery

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

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