A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments

Biswas, Arpan; Liu, Yongtao; Creange, Nicole; Liu, Yu-Chen; Jesse, Stephen; Yang, Jan-Chi; Kalinin, Sergei V.; Ziatdinov, Maxim A.; Vasudevan, Rama K.

doi:10.1038/s41524-023-01191-5

Title: A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments

Journal Article · 03 February 2024 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-023-01191-5 · OSTI ID:2439898

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^[2]; Liu, Yu-Chen ^[3];

^[1]; Yang, Jan-Chi ^[3];

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Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
Tjoapack LLC, Clinton, TN (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
National Cheng Kung Univ., Tainan City (Taiwan)
Univ. of Tennessee, Knoxville, TN (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined a priori with the ability to shift the trajectory of the optimization based on human-identified findings during the experiment is lacking. Thus, to highlight the best of both human operators and AI-driven experiments, here we present the development of a human–AI collaborated experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly with human real-time feedback. Here, the human guidance overpowers AI at early iteration when prior knowledge (uncertainty) is minimal (higher), while the AI overpowers the human during later iterations to accelerate the process with the human-assessed goal. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and in real-time on an atomic force microscope, with human assessment to find symmetric hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human–AI approaches for curiosity driven exploration of systems across experimental domains.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC05-00OR22725; SC0021118

OSTI ID:: 2439898

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 10; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

References (50)

Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy Roccapriore, Kevin M.; Kalinin, Sergei V.; Ziatdinov, Maxim Advanced Science, Vol. 9, Issue 36 https://doi.org/10.1002/advs.202203422	journal	November 2022
Automated Experiments of Local Non‐Linear Behavior in Ferroelectric Materials Liu, Yongtao; Kelley, Kyle P.; Vasudevan, Rama K. Small, Vol. 18, Issue 48 https://doi.org/10.1002/smll.202204130	journal	October 2022
Using Gaussian Processes to Optimize Expensive Functions Frean, Marcus; Boyle, Phillip AI 2008: Advances in Artificial Intelligence https://doi.org/10.1007/978-3-540-89378-3_25	book	January 2008
Sequential Model-Based Optimization for General Algorithm Configuration Hutter, Frank; Hoos, Holger H.; Leyton-Brown, Kevin Lecture Notes in Computer Science https://doi.org/10.1007/978-3-642-25566-3_40	book	January 2011
Machine learning for high-throughput experimental exploration of metal halide perovskites Ahmadi, Mahshid; Ziatdinov, Maxim; Zhou, Yuanyuan Joule, Vol. 5, Issue 11 https://doi.org/10.1016/j.joule.2021.10.001	journal	November 2021
Multi-objective Bayesian materials discovery: Application on the discovery of precipitation strengthened NiTi shape memory alloys through micromechanical modeling Solomou, Alexandros; Zhao, Guang; Boluki, Shahin Materials & Design, Vol. 160 https://doi.org/10.1016/j.matdes.2018.10.014	journal	December 2018
Autonomous experimentation systems for materials development: A community perspective Stach, Eric; DeCost, Brian; Kusne, A. Gilad Matter, Vol. 4, Issue 9 https://doi.org/10.1016/j.matt.2021.06.036	journal	September 2021
COMBO: An efficient Bayesian optimization library for materials science Ueno, Tsuyoshi; Rhone, Trevor David; Hou, Zhufeng Materials Discovery, Vol. 4 https://doi.org/10.1016/j.md.2016.04.001	journal	June 2016
Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models Dhamala, Jwala; Bajracharya, Pradeep; Arevalo, Hermenegild J. Medical Image Analysis, Vol. 62 https://doi.org/10.1016/j.media.2020.101670	journal	May 2020
Rapid Characterization and Parameter Space Exploration of Perovskites Using an Automated Routine Reinhardt, Elisabeth; Salaheldin, Ahmed M.; Distaso, Monica ACS Combinatorial Science, Vol. 22, Issue 1 https://doi.org/10.1021/acscombsci.9b00068	journal	December 2019
Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy Kalinin, Sergei V.; Ziatdinov, Maxim; Hinkle, Jacob ACS Nano, Vol. 15, Issue 8 https://doi.org/10.1021/acsnano.1c02104	journal	July 2021
Efficient Global Optimization of Expensive Black-Box Functions Jones, Donald R.; Schonlau, Matthias; Welch, William J. Journal of Global Optimization, Vol. 13, Issue 4, p. 455-492 https://doi.org/10.1023/A:1008306431147	journal	January 1998
A Taxonomy of Global Optimization Methods Based on Response Surfaces Jones, Donald R. Journal of Global Optimization, Vol. 21, Issue 4, p. 345-383 https://doi.org/10.1023/A:1012771025575	journal	December 2001
A law of comparative judgment. Thurstone, L. L. Psychological Review, Vol. 34, Issue 4 https://doi.org/10.1037/h0070288	journal	January 1927
Twisted oxide lateral homostructures with conjunction tunability Wu, Ping-Chun; Wei, Chia-Chun; Zhong, Qilan Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-30321-8	journal	May 2022
Autonomous scanning probe microscopy investigations over WS2 and Au{111} Thomas, John C.; Rossi, Antonio; Smalley, Darian npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00777-9	journal	May 2022
Uncovering material deformations via machine learning combined with four-dimensional scanning transmission electron microscopy Shi, Chuqiao; Cao, Michael C.; Rehn, Sarah M. npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00793-9	journal	May 2022
Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns Munshi, Joydeep; Rakowski, Alexander; Savitzky, Benjamin H. npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00939-9	journal	December 2022
Human–machine collaboration for improving semiconductor process development Kanarik, Keren J.; Osowiecki, Wojciech T.; Lu, Yu Nature, Vol. 616, Issue 7958 https://doi.org/10.1038/s41586-023-05773-7	journal	March 2023
Multi-objective Optimization for Materials Discovery via Adaptive Design Gopakumar, Abhijith M.; Balachandran, Prasanna V.; Xue, Dezhen Scientific Reports, Vol. 8, Issue 1 https://doi.org/10.1038/s41598-018-21936-3	journal	February 2018
Artificial-intelligence-driven scanning probe microscopy Krull, A.; Hirsch, P.; Rother, C. Communications Physics, Vol. 3, Issue 1 https://doi.org/10.1038/s42005-020-0317-3	journal	March 2020
Experimental discovery of structure–property relationships in ferroelectric materials via active learning Liu, Yongtao; Kelley, Kyle P.; Vasudevan, Rama K. Nature Machine Intelligence, Vol. 4, Issue 4 https://doi.org/10.1038/s42256-022-00460-0	journal	April 2022
AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy Ziatdinov, Maxim; Ghosh, Ayana; Wong, Chun Yin Nature Machine Intelligence, Vol. 4, Issue 12 https://doi.org/10.1038/s42256-022-00555-8	journal	December 2022
The rise of self-driving labs in chemical and materials sciences Abolhasani, Milad; Kumacheva, Eugenia Nature Synthesis, Vol. 2, Issue 6 https://doi.org/10.1038/s44160-022-00231-0	journal	January 2023
Progress and prospects for accelerating materials science with automated and autonomous workflows Stein, Helge S.; Gregoire, John M. Chemical Science, Vol. 10, Issue 42 https://doi.org/10.1039/C9SC03766G	journal	January 2019
Constrained Bayesian optimization for automatic chemical design using variational autoencoders Griffiths, Ryan-Rhys; Hernández-Lobato, José Miguel Chemical Science, Vol. 11, Issue 2 https://doi.org/10.1039/C9SC04026A	journal	January 2020
Bayesian optimization in continuous spaces via virtual process embeddings Valleti, Mani; Vasudevan, Rama K.; Ziatdinov, Maxim A. Digital Discovery, Vol. 1, Issue 6 https://doi.org/10.1039/D2DD00065B	journal	January 2022
Guided search for desired functional responses via Bayesian optimization of generative model: Hysteresis loop shape engineering in ferroelectrics Kalinin, Sergei V.; Ziatdinov, Maxim; Vasudevan, Rama K. Journal of Applied Physics, Vol. 128, Issue 2 https://doi.org/10.1063/5.0011917	journal	July 2020
Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications Biswas, Arpan; Morozovska, Anna N.; Ziatdinov, Maxim Journal of Applied Physics, Vol. 130, Issue 20 https://doi.org/10.1063/5.0068903	journal	November 2021
Optimizing training trajectories in variational autoencoders via latent Bayesian optimization approach ^* Biswas, Arpan; Vasudevan, Rama; Ziatdinov, Maxim Machine Learning: Science and Technology, Vol. 4, Issue 1 https://doi.org/10.1088/2632-2153/acb316	journal	February 2023
Effect of Surface Ionic Screening on Polarization Reversal and Phase Diagrams in Thin Antiferroelectric Films for Information and Energy Storage Morozovska, Anna N.; Eliseev, Eugene A.; Biswas, Arpan Physical Review Applied, Vol. 16, Issue 4 https://doi.org/10.1103/PhysRevApplied.16.044053	journal	October 2021
Chemical control of polarization in thin strained films of a multiaxial ferroelectric: Phase diagrams and polarization rotation Morozovska, Anna N.; Eliseev, Eugene A.; Biswas, Arpan Physical Review B, Vol. 105, Issue 9 https://doi.org/10.1103/PhysRevB.105.094112	journal	March 2022
Probing the Role of Single Defects on the Thermodynamics of Electric-Field Induced Phase Transitions Kalinin, S. V.; Jesse, S.; Rodriguez, B. J. Physical Review Letters, Vol. 100, Issue 15 https://doi.org/10.1103/PhysRevLett.100.155703	journal	April 2008
Bayesian Optimization for Adaptive Experimental Design: A Review Greenhill, Stewart; Rana, Santu; Gupta, Sunil IEEE Access, Vol. 8 https://doi.org/10.1109/ACCESS.2020.2966228	journal	January 2020
A statistical method for global optimization Cox, D. D.; John, S. [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics https://doi.org/10.1109/ICSMC.1992.271617	conference	January 1992
Taking the Human Out of the Loop: A Review of Bayesian Optimization Shahriari, Bobak; Swersky, Kevin; Wang, Ziyu Proceedings of the IEEE, Vol. 104, Issue 1 https://doi.org/10.1109/JPROC.2015.2494218	journal	January 2016
A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise Kushner, H. J. Journal of Basic Engineering, Vol. 86, Issue 1 https://doi.org/10.1115/1.3653121	journal	March 1964
An Approach to Bayesian Optimization for Design Feasibility Check on Discontinuous Black-Box Functions Biswas, Arpan; Hoyle, Christopher Journal of Mechanical Design, Vol. 143, Issue 3 https://doi.org/10.1115/1.4049742	journal	February 2021
A Multi-Objective Bayesian Optimization Approach Using the Weighted Tchebycheff Method Biswas, Arpan; Fuentes, Claudio; Hoyle, Christopher Journal of Mechanical Design, Vol. 144, Issue 1 https://doi.org/10.1115/1.4051787	journal	August 2021
srMO-BO-3GP: A Sequential Regularized Multi-Objective Constrained Bayesian Optimization for Design Applications Tran, Anh; Eldred, Mike; McCann, Scott ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 9: 40th Computers and Information in Engineering Conference (CIE) https://doi.org/10.1115/DETC2020-22184	conference	November 2020
A Survey on High-dimensional Gaussian Process Modeling with Application to Bayesian Optimization Binois, Mickaël; Wycoff, Nathan ACM Transactions on Evolutionary Learning and Optimization, Vol. 2, Issue 2 https://doi.org/10.1145/3545611	journal	June 2022
Bayesian Optimization in a Billion Dimensions via Random Embeddings Wang, Ziyu; Hutter, Frank; Zoghi, Masrour Journal of Artificial Intelligence Research, Vol. 55 https://doi.org/10.1613/jair.4806	journal	January 2016
Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions Soldatov, Mikhail A.; Butova, Vera V.; Pashkov, Danil Nanomaterials, Vol. 11, Issue 3 https://doi.org/10.3390/nano11030619	journal	March 2021
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning Brochu, Eric; Cora, Vlad M.; de Freitas, Nando arXiv https://doi.org/10.48550/arXiv.1012.2599	preprint	January 2010
Adam: A Method for Stochastic Optimization Kingma, Diederik P.; Ba, Jimmy arXiv https://doi.org/10.48550/arXiv.1412.6980	preprint	January 2014
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning Grosnit, Antoine; Tutunov, Rasul; Maraval, Alexandre Max arXiv https://doi.org/10.48550/arXiv.2106.03609	preprint	January 2021
Bayesian Optimization in Materials Science: A Survey Kotthoff, Lars; Wahab, Hud; Johnson, Patrick arXiv https://doi.org/10.48550/arXiv.2108.00002	preprint	January 2021
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning Ziatdinov, Maxim; Liu, Yongtao; Kalinin, Sergei V. arXiv https://doi.org/10.48550/arXiv.2203.10181	preprint	January 2022
Learning the right channel in multimodal imaging: automated experiment in Piezoresponse Force Microscopy Liu, Yongtao; Vasudevan, Rama K.; Kelley, Kyle P. arXiv https://doi.org/10.48550/arXiv.2207.03039	preprint	January 2022
scikit-image: image processing in Python van der Walt, Stéfan; Schönberger, Johannes L.; Nunez-Iglesias, Juan PeerJ, Vol. 2 https://doi.org/10.7717/peerj.453	journal	January 2014

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