The future of self-driving laboratories: from human in the loop interactive AI to gamification

Hysmith, Holland; Foadian, Elham; Padhy, Shakti P.; Kalinin, Sergei V.; Moore, Rob G.; Ovchinnikova, Olga S.; Ahmadi, Mahshid

doi:10.1039/d4dd00040d

Title: The future of self-driving laboratories: from human in the loop interactive AI to gamification

Journal Article · 14 March 2024 · Digital Discovery

DOI: https://doi.org/10.1039/d4dd00040d · OSTI ID:2332890

^[1]; Foadian, Elham ^[1]; Padhy, Shakti P. ^[1]; Kalinin, Sergei V. ^[2]; Moore, Rob G. ^[3]; Ovchinnikova, Olga S. ^[4];

^[1]

University of Tennessee, Knoxville, TN (United States)
University of Tennessee, Knoxville, TN (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
University of Tennessee, Knoxville, TN (United States); Thermo Fisher Scientific, Carlsbad, CA (United States)

Recent developments in artificial intelligence (AI) and machine learning (ML), implemented through self-driving laboratories (SDLs), are rapidly creating unprecedented opportunities for the accelerated discovery and optimization of materials. This paper provides a joint analysis of SDLs from both academic and industry perspectives, highlighting the importance of integrating human intelligence in these systems. It discusses the necessity of careful planning in SDL design across physical, data, and workflow dimensions, including instrumental setup, experimental workflow, data management, and human–SDL interaction. The significance of integrating human input within SDLs, especially as the focus shifts from individual tools and tasks to the creation and management of complex workflows, is emphasized. The paper stresses on the crucial role of reward function design in developing forward-looking workflows and examines the interplay between hardware evolution, ML application across chemical processes, and the influence of reward systems in research. Ultimately, the article advocates for a future where SDLs blend human intuition in hypothesis formulation with AI's precision, speed, and data-handling capabilities.

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Research Organization:: Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Nuclear Physics (NP); USDOE Office of Science (SC), Fusion Energy Sciences (FES); National Science Foundation (NSF); Alfred P. Sloan Foundation

Grant/Contract Number:: SC0021118; AC05-00OR22725; 2043205; FG-2022-18275; AC05-76RL01830

OSTI ID:: 2332890

Alternate ID(s):: OSTI ID: 2406774; OSTI ID: 2438546; OSTI ID: 2507329

Report Number(s):: PNNL-SA-193852

Journal Information:: Digital Discovery, Vol. 3, Issue 4; ISSN 2635-098X

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United States

Language:: English

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Exploring order parameters and dynamic processes in disordered systems via variational autoencoders Kalinin, Sergei V.; Dyck, Ondrej; Jesse, Stephen Science Advances, Vol. 7, Issue 17 https://doi.org/10.1126/sciadv.abd5084	journal	April 2021
Exploring the Evolution of Metal Halide Perovskites via Latent Representations of the Photoluminescent Spectra Sanchez, Sheryl; Liu, Yongtao; Yang, Jonghee Advanced Intelligent Systems, Vol. 5, Issue 5 https://doi.org/10.1002/aisy.202200340	journal	January 2023
SELFIES and the future of molecular string representations Krenn, Mario; Ai, Qianxiang; Barthel, Senja Patterns, Vol. 3, Issue 10 https://doi.org/10.1016/j.patter.2022.100588	journal	October 2022
Equation of state, phase transitions, and band-gap closure in PbCl2 and SnCl2 Smart, T. J.; O’Bannon, E. F.; Diamond, M. R. Physical Review B, Vol. 107, Issue 13 https://doi.org/10.1103/PhysRevB.107.134113	journal	April 2023
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis Dulac-Arnold, Gabriel; Levine, Nir; Mankowitz, Daniel J. Machine Learning, Vol. 110, Issue 9 https://doi.org/10.1007/s10994-021-05961-4	journal	April 2021
An empirical investigation of the challenges of real-world reinforcement learning Dulac-Arnold, Gabriel; Levine, Nir; Mankowitz, Daniel J. arXiv https://doi.org/10.48550/arXiv.2003.11881	preprint	January 2020
Emergent autonomous scientific research capabilities of large language models Boiko, Daniil A.; MacKnight, Robert; Gomes, Gabe arXiv https://doi.org/10.48550/arXiv.2304.05332	preprint	January 2023
Science in the age of large language models Birhane, Abeba; Kasirzadeh, Atoosa; Leslie, David Nature Reviews Physics, Vol. 5, Issue 5 https://doi.org/10.1038/s42254-023-00581-4	journal	April 2023
Natural language processing models that automate programming will transform chemistry research and teaching Hocky, Glen M.; White, Andrew D. Digital Discovery, Vol. 1, Issue 2 https://doi.org/10.1039/D1DD00009H	journal	January 2022
Assessment of chemistry knowledge in large language models that generate code White, Andrew D.; Hocky, Glen M.; Gandhi, Heta A. Digital Discovery, Vol. 2, Issue 2 https://doi.org/10.1039/D2DD00087C	journal	January 2023
Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries Ziatdinov, Maxim A.; Liu, Yongtao; Morozovska, Anna N. Advanced Materials, Vol. 34, Issue 20 https://doi.org/10.1002/adma.202201345	journal	April 2022
Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning Ziatdinov, Maxim; Liu, Yongtao; Kelley, Kyle ACS Nano, Vol. 16, Issue 9 https://doi.org/10.1021/acsnano.2c05303	journal	September 2022
Driving school for self-driving labs Snapp, Kelsey L.; Brown, Keith A. Digital Discovery, Vol. 2, Issue 5 https://doi.org/10.1039/D3DD00150D	journal	January 2023
Flexible automation accelerates materials discovery MacLeod, Benjamin P.; Parlane, Fraser G. L.; Brown, Amanda K. Nature Materials, Vol. 21, Issue 7 https://doi.org/10.1038/s41563-021-01156-3	journal	December 2021

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