arXiv Machine Learning July 1, 2026 By Jawad Chowdhury, Ganesh Narasimha, Jan-Chi Yang, Yongtao Liu, Rama Vasudevan

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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 AI

5d ago

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

6d ago

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

Related stories

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

Multitask Scanning Probe Microscopy

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

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

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

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