Abstract
Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. Here, an active learning approach based on conavigation of the hypothesis and experimental spaces is introduced. This is realized by combining the structured Gaussian processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. This approach is demonstrated for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO3 using piezoresponse force microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis generation are available.
| Original language | English |
|---|---|
| Article number | 2201345 |
| Journal | Advanced Materials |
| Volume | 34 |
| Issue number | 20 |
| DOIs | |
| State | Published - May 19 2022 |
Funding
The development of the hypothesis learning algorithm was supported by the Center for Nanophase Materials Sciences (CNMS), which is a U.S. Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory (M.Z.). The experimental deployment of the hypothesis learning was supported by the center for 3D Ferroelectric Microelectronics (3DFeM), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award Number DE\u2010SC0021118 (Y.L. and S.V.K.). A.N.M. was supported by the National Academy of Sciences of Ukraine (the Target Program of Basic Research of the National Academy of Sciences of Ukraine \u201CProspective basic research and innovative development of nanomaterials and nanotechnologies for 2020\u20132024,\u201D Project No. 1/20\u2010H, state registration number: 0120U102306) and received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska\u2010Curie Grant Agreement No. 778070. The work at the University of Maryland was supported in part by the National Institute of Standards and Technology Cooperative Agreement 70NANB17H301. The authors gratefully acknowledge Dr. Bobby Sumpter for careful reading and editing of the manuscript. The development of the hypothesis learning algorithm was supported by the Center for Nanophase Materials Sciences (CNMS), which is a U.S. Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory (M.Z.). The experimental deployment of the hypothesis learning was supported by the center for 3D Ferroelectric Microelectronics (3DFeM), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award Number DE-SC0021118 (Y.L. and S.V.K.). A.N.M. was supported by the National Academy of Sciences of Ukraine (the Target Program of Basic Research of the National Academy of Sciences of Ukraine \u201CProspective basic research and innovative development of nanomaterials and nanotechnologies for 2020\u20132024,\u201D Project No. 1/20-H, state registration number: 0120U102306) and received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie Grant Agreement No. 778070. The work at the University of Maryland was supported in part by the National Institute of Standards and Technology Cooperative Agreement 70NANB17H301. The authors gratefully acknowledge Dr. Bobby Sumpter for careful reading and editing of the manuscript.
Keywords
- active learning
- combinatorial library
- ferroelectric
- hypothesis learning
- scanning probe microscopy