Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 621-636 |
| Number of pages | 16 |
| Journal | Digital Discovery |
| Volume | 3 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 1 2024 |
Funding
HH was supported by the DOE Office of Science Research Program for Microelectronics Codesign (sponsored by ASCR, BES, HEP, NP, and FES) through the Abisko Project with program managers Robinson Pino (ASCR), Hal Finkel (ASCR), and Andrew Schwartz (BES). EF and MA acknowledge support from the National Science Foundation (NSF), award number no. 2043205 and Alfred P. Sloan Foundation, award no. FG-2022-18275. RGM acknowledges support from the INTERSECT Initiative as part of the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This effort (SVK; ML and human in the loop) was supported as part of the center for 3D Ferroelectric Microelectronics (3DFeM), an Energy Frontier Research Center funded by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences under award number DE-SC0021118.