arXiv Machine Learning

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

arXiv:2603. 29135v2 Announce Type: replace Abstract: Autonomous experimental systems are increasingly used in materials research to accelerate scientific discovery, but their performance is often limited by low-quality, noisy data.

arXiv Machine Learning

Jul 17

Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields

arXiv:2607. 14486v1 Announce Type: cross Abstract: Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning workflows.

By Sheng Bi, Yi-Ze Wang, Jun Cheng

fine-tuning

arXiv Machine Learning

Aug 11

Multitask Scanning Probe Microscopy

arXiv:2608. 09104v1 Announce Type: cross Abstract: Scanning probe microscopy provides nanoscale access to structural, electrical, electromechanical, magnetic, and mechanical properties of materials.

By Aditya Raghavan, Yu Liu, Ian Mercer, JP Maria, Sergei Kalinin

agents multimodal

arXiv Machine Learning

Jun 15

Closed-loop discovery of out-of-distribution processing protocols by evolutionary search and uncertainty-aware learning

arXiv:2606. 13859v1 Announce Type: cross Abstract: Many materials and chemical systems exhibit history-dependent responses, where functional outcomes are governed not only by final-state variables but by the time-dependent sequence of fields, temperatures, or chemical potentials applied during operation.

By Yu Liu, Stanislav Udovenko, Ching-Che Lin, Jaegyu Kim, Lane W. Martin, Susan Trolier-McKinstry, Sergei V. Kalinin

safety

arXiv Machine Learning

Sep 14

QuPAINT: Physics-Aware Multimodal Reasoning for Quantum Material Characterization

QuPAINT is a physics‑aware multimodal framework designed to characterize two‑dimensional quantum materials using optical microscopy. It employs the Synthetic Materials Framework (Synthia) to generate diverse synthetic images that preserve layer‑dependent optical behavior, and builds the QMat‑Instruct dataset with image‑specific reasoning traces. The framework integrates these signals through Physics‑Informed Attention (PIA) and is evaluated on the newly introduced QF‑Bench benchmark, achieving state‑of‑the‑art performance for flake detection and demonstrating improved spatial grounding and confidence calibration.

By Sankalp Pandey, Xuan-Bac Nguyen, Hoang-Quan Nguyen, Tim Faltermeier, Nicholas Borys, Hugh Churchill, Khoa Luu

multimodal benchmarks

arXiv AI

Aug 14

Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples

arXiv:2608. 13341v1 Announce Type: cross Abstract: Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging.

By Yusen Tan, Yixuan Chen, Zheng Fang, Pan Liu, Yifan Li, Qinyu Guo, Zhedong Lin, Yuqiang Li, Xiangxiang Zeng, Tong Wang, Jun Xia

fine-tuning safety

arXiv AI

Jun 9

Context-Aware Deep Learning for Defect Classification in Atomic-Resolution STEM

arXiv:2606. 09419v1 Announce Type: cross Abstract: Artificial intelligence is rapidly advancing materials characterization, yet most applications in electron microscopy rely solely on image contrast, overlooking the chemical and experimental context that shapes image formation.

By Jiadong Dan, Cheng Zhang, Leyi Loh, Ivan Verzhbitskiy, Yuan Chen, Goki Eda, Michel Bosman, N. Duane Loh

agents multimodal

Hugging Face Trending Papers

Aug 13

Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples

Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging. Conventional interpretation is labor-intensive, relies on prior knowledge and reference spectra, and is difficult to scale, whereas most machine-learning methods are tailored to individual tasks or datasets, require large labeled training sets, and transfer poorly across analytical objectives and experimental datasets.

fine-tuning safety

arXiv Machine Learning

Jun 30

STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy

arXiv:2606. 29592v1 Announce Type: new Abstract: A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition.

By Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban

llms agents reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Sep 2

AdaptNTK: Adaptive Uncertainty Quantification and Active Learning for Neural Network Potentials

arXiv:2609.00488v1 Announce Type: new Abstract: Machine learning interatomic potentials bridge the gap between quantum chemical precision and classical computational speed, enabling molecular dynamic...

By Prajwal Ananth, Shuwen Yue

Hugging Face Trending Papers

Jun 28

STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy

A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition. We present evidence to the contrary in scanning transmission electron microscopy (STEM), an atomic-resolution imaging modality whose every measurement deposits damaging electron dose.

llms agents reinforcement-learning multimodal benchmarks

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

Sep 1

AutoREC: A reinforcement learning platform for equivalent circuit model generation

AutoREC is an open‑source Python platform that uses reinforcement learning to automatically generate equivalent circuit models (ECMs) from electrochemical impedance spectroscopy (EIS) data. The platform frames ECM generation as a Markov decision process, training a Double Deep Q‑Network agent that iteratively modifies circuit topologies based on state, actions, and model feedback. It supports end‑to‑end workflows—including EIS preprocessing, agent training, ECM generation, and visualization—and has been demonstrated on synthetic datasets and real experimental spectra from batteries, corrosion, oxygen evolution, and CO₂ reduction systems.

By Ali Jaberi (Clean Energy Innovation Research Centre, National Research Council Canada, Mississauga, ON, Canada), Yonatan Kurniawan (Department of Materials Science and Engineering, University of Toronto, Toronto, ON, Canada), Robert Black (Clean Energy Innovation Research Centre, National Research Council Canada, Mississauga, ON, Canada), Shayan Mousavi M. (Clean Energy Innovation Research Centre, National Research Council Canada, Mississauga, ON, Canada), Kabir Verma (Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada), Zoya Sadighi (Clean Energy Innovation Research Centre, National Research Council Canada, Mississauga, ON, Canada), Santiago Miret (Lila Sciences, San Francisco, CA, USA), Jason Hattrick-Simpers (Department of Materials Science and Engineering, University of Toronto, Toronto, ON, Canada)

agents reinforcement-learning robotics

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