Hugging Face Trending Papers
Decision-Focused Active Learning for Scale-Aware Critical-Materials Recovery
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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.
arXiv Machine Learning
Aug 4
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 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.
arXiv Machine Learning
Sep 22
SCALE: Simulation-Calibrated Amortized Learning for Energy Materials (A hybrid architecture connecting deterministic modeling, real-world data, and transformer-scale inference for accelerated energy-materials discovery)
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.
arXiv AI
Jun 15
Korzhinskii-Net: Physics-Informed Neural Network for Sub-Surface Mineral Prospectivity Modelling
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.
arXiv Machine Learning
Jun 29
Non-Linear Model-Based Sequential Decision-Making in Agriculture
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.