109 citations · 519 across the 47 of their papers we have counts for
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Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries
Maxim Ziatdinov, Yongtao Liu, Anna N. Morozovska +4
Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and elect…
Automated experiment in 4D-STEM: exploring emergent physics and structural behaviors
Kevin M. Roccapriore, Ondrej Dyck, Mark P. Oxley +2
Automated experiments in 4D Scanning Transmission Electron Microscopy are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric…
Exploring causal physical mechanisms via non-gaussian linear models and deep kernel learning: applications for ferroelectric domain structures
Yongtao Liu, Maxim Ziatdinov, Sergei V. Kalinin
Rapid emergence of the multimodal imaging in scanning probe, electron, and optical microscopies have brought forth the challenge of understanding the information contained in these…
Describing condensed matter from atomically resolved imaging data: from structure to generative and causal models
Sergei V. Kalinin, Ayana Ghosh, Rama Vasudevan +1
The development of high-resolution imaging methods such as electron and scanning probe microscopy and atomic probe tomography have provided a wealth of information on structure and…
Bridging microscopy with molecular dynamics and quantum simulations: An AtomAI based pipeline
Ayana Ghosh, Maxim Ziatdinov, Ondrej Dyck +2
Recent advances in (scanning) transmission electron microscopy have enabled routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally o…
Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications
Arpan Biswas, Anna N. Morozovska, Maxim Ziatdinov +2
Optimization of materials performance for specific applications often requires balancing multiple aspects of materials functionality. Even for the cases where generative physical m…
Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process
Maxim Ziatdinov, Ayana Ghosh, Sergei V. Kalinin
Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces…
Physics discovery in nanoplasmonic systems via autonomous experiments in Scanning Transmission Electron Microscopy
Kevin M. Roccapriore, Sergei V. Kalinin, Maxim Ziatdinov
Physics-driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here we develop and experimentally implement a dee…
Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy
Sergei V. Kalinin, Maxim A. Ziatdinov, Jacob Hinkle +6
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials pred…