Autonomous Experimentation

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15 papers in the last four weeks, up 114% on the four weeks before. 0.1% of all new papers.

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Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
Apr 26, 2026William RatcliffActive Learning Materials Science
NIST Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
Autonomous Reliability Qualification of Ga $_2$ O $_3$ -based Hydrogen and Temperature Sensors via Safe Active Learning
Apr 22, 2026Davi Febba, William A. Callahan, Anna Sacchi +1Active Learning Autonomous Experimentation
Materials Science Center, National Laboratory of the Rockies (NLR), Golden, CO 80401, USA
Quality-Controlled Active Learning via Gaussian Processes for Robust Structure-Property Learning in Autonomous Microscopy
Mar 31, 2026Jawad Chowdhury, Ganesh Narasimha, Jan-Chi Yang +4Active Learning Physics-Informed ML
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA. · Department of Physics, National Cheng Kung University, Tainan, 70101, Taiwan.
Agentic Artificial Intelligence for Multistage Physics Experiments at a Large-Scale User Facility Particle Accelerator
Sep 21, 2025Thorsten Hellert, Drew Bertwistle, Simon C. Leemann +2AI for Science Agentic Workflows
Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
Accelerating battery research with an interoperable interface between FINALES and Kadi4Mat
Date pendingGiovanna Tosato (Karlsruhe Institute of Technology), Leon Merker (Karlsruhe Institute of Technology, Helmholtz Institute Ulm +4Materials Science Scientific Workflow Automation
Institute for Applied Materials - Microstructure Modeling and Simulation (IAM-MMS), Karlsruhe Institute of Technology (KIT), Germany · Helmholtz Institute Ulm (HIU), Karlsruhe Institute of Technology (KIT), Germany · TUM School of Natural Sciences, Department of Chemistry, Chair of Digital Catalysis, Munich +6

Apr 26, 2026, cond-mat.mtrl-sciJ/K move · Enter open · S save

Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy

William Ratcliff

NIST Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA

Active Learning Materials Science Bayesian Experimental Design Autonomous Experimentation

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Autonomous neutron spectroscopy must solve three distinct tasks: detection (where is the signal?), inference (which Hamiltonian governs it?), and refinement (what are the parameters?). No single controller solves all three equally well. We present TAS-AI, a hybrid agnostic-to-physics-informed framework for autonomous triple-axis spin-wave spectroscopy that separates these tasks explicitly. In blind reconstruction benchmarks, model-agnostic methods such as random sampling, coarse grids, and Gaussian-process mappers reach a global error threshold more reliably and with fewer measurements than physics-informed planning, supporting the claim that discovery and inference are distinct tasks requiring distinct controllers. Once signal structure is localized, the physics-informed stage performs in-loop Hamiltonian discrimination and parameter refinement: in a controlled square-lattice test between nearest-neighbor-only and J1-J2 Hamiltonians, TAS-AI reaches a decisive AIC-derived evidence ratio (>100) in fewer than 10 measurements, while motion-aware scheduling cuts wall-clock time by 32% at a fixed measurement budget. We also identify a failure mode of posterior-weighted design, algorithmic myopia, in which the planner over-refines the current leading model while under-sampling low-intensity falsification probes. A constrained falsification channel sharply reduces time spent committed to the wrong model and accelerates correct model selection without modifying the Bayesian inference engine. In controlled two-model ablations, both a deterministic top-two max-disagreement rule and an LLM-based audit committee achieve this gain under identical constraints. We demonstrate the full workflow in silico using a high-fidelity digital twin and provide an open-source Python implementation.

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Latest papers 95

Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy

Autonomous Reliability Qualification of Ga2_22​O3_33​-based Hydrogen and Temperature Sensors via Safe Active Learning

Quality-Controlled Active Learning via Gaussian Processes for Robust Structure-Property Learning in Autonomous Microscopy

Agentic Artificial Intelligence for Multistage Physics Experiments at a Large-Scale User Facility Particle Accelerator

Accelerating battery research with an interoperable interface between FINALES and Kadi4Mat

Autonomous Reliability Qualification of Ga $_2$ O $_3$ -based Hydrogen and Temperature Sensors via Safe Active Learning