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
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.
By Jingyang Bai, Zijia Wang, Xiangyi Long, Marcos Millan, Binjian Nie, Mingyue Ding
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
SCALE (Simulation‑Calibrated Amortized Learning for Energy Materials) is a physics‑grounded learning architecture that fuses deterministic scientific operators, experimental calibration, and transformer‑scale inference to accelerate the discovery of energy materials. It converts expensive mechanistic computations and measured data into reusable models for rapid screening, ranking, inverse design, and active learning. In a case study on solid‑state metal‑hydride hydrogen‑storage, SCALE calibrated a phase‑equilibrium capacity operator against 381 experimental anchors, generated 5,000 teacher labels, and used a 2.90‑million‑parameter edge‑biased graph transformer to achieve high surrogate fidelity (MAE 0.0582 wt% H₂, RMSE 0.0833 wt% H₂, R² 0.9927, Pearson r 0.9963) while reducing per‑candidate screening cost by 10⁷–10⁸ times.
By Kuan Huang, Bo Bai
arXiv:2606. 13695v1 Announce Type: cross Abstract: Mineral prospectivity modelling (MPM) underpins exploration economics, yet most operational pipelines reduce to data-driven classifiers trained on shallow surface proxies.
By Boris Kriuk
arXiv:2509. 01924v4 Announce Type: replace-cross Abstract: Agricultural decision-making faces a dual challenge: sustaining high yields to meet global food security needs while reducing the environmental impacts of input use, including fertilizer losses and other agrochemical applications such as herbicides, insecticides, and fungicides.
By Sakshi Arya, Wentao Lin
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:2605. 02405v2 Announce Type: replace Abstract: Closed-loop management of geological CO2 storage requires control policies that adapt to uncertain reservoir behavior while relying on observations that are realistically available during operation.
By Sofianos Panagiotis Fotias, Vassilis Gaganis
arXiv:2606. 09037v1 Announce Type: new Abstract: Interior permanent magnet synchronous motor (IPMSM) design requires balancing conflicting objectives and multi-physics constraints, while modern optimization workflows face three bottlenecks: manual problem setup, high finite element analysis (FEA) cost, and unreliable surrogate-based search in sparse or out-of-distribution regions.
By Jinseong Han, Sunwoong Yang, Namwoo Kang
arXiv:2607. 21480v1 Announce Type: new Abstract: An initial high-recall stage in an empirical pipeline decides which items pass to later review, labelling, or modelling, and relevant items it misses are lost to every subsequent stage.
By Martin Anthony, Kaveh Salehzadeh Nobari
arXiv:2608. 19790v1 Announce Type: new Abstract: Discovering materials with desirable properties often requires searching large candidate spaces while experimental or computational evaluations remain costly.
By Dino-Rober Demir, Florian Le Bronnec, Rio Yokota
arXiv:2607. 05806v1 Announce Type: new Abstract: Training data for machine learning is routinely collected by a selection process the model never sees: loans are observed only when granted, outcomes only when a test was ordered.
By Gunner Levi Howe