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==Introduction== | ==Introduction== | ||
The integration of [[Laboratory automation|robotic]] experiments and [[artificial intelligence]] (AI) is essential to realize automated materials exploration. If an AI system can take on some [[information]] tasks conventionally performed by human researchers, robotic systems can then execute the required physical tasks and experiments for materials exploration can proceed automatically. Such a platform may be expected to discover many novel materials and lead to substantial innovation in [[materials science]]. In recent years, significant progress has been made in the development of AI techniques and robotic devices suitable for materials exploration. | |||
Since the launch of the Materials Genome Initiative [1], AI techniques have been actively used for materials exploration. [2–4] In general, materials exploration can be regarded as the problem of finding optimal materials from among a materials search space. The elements to be used in the search space must be configured, along with its composition range, process parameter range, and so forth. To solve this problem, black-box optimization methods are useful [5], and various methods have been developed and applied to fit various needs. Bayesian optimization (BO) is among the most frequently used methods in materials science. [6–8]. In this method, promising materials can be selected in the materials search space using the predictions of their properties and the uncertainty of these predictions evaluated by Gaussian process regression. Using BO, various real materials, such as Li-ion conductive materials [9], multilayered metamaterials [10], halide perovskite [11], superalloys [12], and electrolytes [13] have been explored. BO is also used for the automated analysis of materials [14–15]. In addition, many methods have been proposed for black-box optimization in materials exploration, such as genetic algorithms [16–17], Monte Carlo tree search [18], rare event sampling [19], and algorithms using an Ising machine. [20–22] In the future, many more innovative methods are expected to be developed. | |||
Revision as of 23:05, 15 September 2023
| Full article title | NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science |
|---|---|
| Journal | Science and Technology of Advanced Materials: Methods |
| Author(s) | Tamura, Ryo; Tsuda, Koji; Matsuda, Shoichi |
| Author affiliation(s) | The University of Tokyo, National Institute for Materials Science |
| Primary contact | Email: tamura dot ryo at nims dot go dot jp |
| Year published | 2023 |
| Volume and issue | 3(1) |
| Article # | 2232297 |
| DOI | 10.1080/27660400.2023.2232297 |
| ISSN | 2766-0400 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.tandfonline.com/doi/full/10.1080/27660400.2023.2232297 |
| Download | https://www.tandfonline.com/doi/pdf/10.1080/27660400.2023.2232297 (PDF) |
 |
This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
NIMS-OS (NIMS Orchestration System) is a Python library created to realize a closed loop of robotic experiments and artificial intelligence (AI) without human intervention for automated materials exploration. It uses various combinations of modules to operate autonomously. Each module acts as an AI for materials exploration or a controller for a robotic experiments. As AI techniques, optimization tools for PHYSics based on Bayesian Optimization (PHYSBO), BoundLess Objective-free eXploration (BLOX), phase diagram construction (PDC), and random exploration (RE) methods can be used. Moreover, a system called NIMS Automated Robotic Electrochemical Experiments (NAREE) is available as a set of robotic experimental equipment. Visualization tools for the results are also included, which allows users to check the optimization results in real time. Newly created modules for AI and robotic experiments can be added easily to extend the functionality of the system. In addition, we developed a graphical user inferface (GUI)-driven application to control NIMS-OS. To demonstrate the operation of NIMS-OS, we consider an automated exploration for new electrolytes. NIMS-OS is available at https://github.com/nimsos-dev/nimsos.
Keywords: NIMS-OS, robotic experiments, artificial intelligence, electrochemistry, materials informatics
Introduction
The integration of robotic experiments and artificial intelligence (AI) is essential to realize automated materials exploration. If an AI system can take on some information tasks conventionally performed by human researchers, robotic systems can then execute the required physical tasks and experiments for materials exploration can proceed automatically. Such a platform may be expected to discover many novel materials and lead to substantial innovation in materials science. In recent years, significant progress has been made in the development of AI techniques and robotic devices suitable for materials exploration.
Since the launch of the Materials Genome Initiative [1], AI techniques have been actively used for materials exploration. [2–4] In general, materials exploration can be regarded as the problem of finding optimal materials from among a materials search space. The elements to be used in the search space must be configured, along with its composition range, process parameter range, and so forth. To solve this problem, black-box optimization methods are useful [5], and various methods have been developed and applied to fit various needs. Bayesian optimization (BO) is among the most frequently used methods in materials science. [6–8]. In this method, promising materials can be selected in the materials search space using the predictions of their properties and the uncertainty of these predictions evaluated by Gaussian process regression. Using BO, various real materials, such as Li-ion conductive materials [9], multilayered metamaterials [10], halide perovskite [11], superalloys [12], and electrolytes [13] have been explored. BO is also used for the automated analysis of materials [14–15]. In addition, many methods have been proposed for black-box optimization in materials exploration, such as genetic algorithms [16–17], Monte Carlo tree search [18], rare event sampling [19], and algorithms using an Ising machine. [20–22] In the future, many more innovative methods are expected to be developed.
References
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. In the original, there are multiple instances of citing research work using the last name of the last author listed, rather than the last name of the first author listed; this may have been a product of Japanese culture tending to read text from right to left. For this version, the last name of the first author was used to be consistent with research norms.
