Journal Article10.1039/d2nr07147a
Bayesian optimisation for efficient material discovery: a mini review.
Yimeng Jin,Priyanka Kumari +1 more
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TL;DR: In this article , a short review aiming at connecting algorithmic advancement to material applications is provided, where open algorithmic challenges are discussed and supported by recent material applications and three exemplary material design problems are analysed to demonstrate how BO could be useful.
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Abstract: Bayesian optimisation (BO) has been increasingly utilised to guide material discovery. While BO is advantageous due to its sample efficiency, flexibility and versatility, it is constrained by a range of core issues including high-dimensional optimisation, mixed search space, multi-objective optimisation and multi-fidelity data. Although various studies have attempted to tackle one or some challenges, a comprehensive BO framework for material discovery is yet to be uncovered. This work provides a short review aiming at connecting algorithmic advancement to material applications. Open algorithmic challenges are discussed and supported by recent material applications. Various open-source packages are compared to assist the selection. Furthermore, three exemplary material design problems are analysed to demonstrate how BO could be useful. The review concludes with an outlook on BO-aided autonomous laboratory.
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Citations
Review of Low-cost Self-driving Laboratories in Chemistry and Materials Science: The "Frugal Twin" Concept
Stanley Lo,Sterling G. Baird,Joshua Schrier,Ben J Blaiszik,Nessa Carson,Ian Foster,Andrés Aguilar‐Granda,Sergei V. Kalinin,Benji Maruyama,Maria Politi,Helen Tran,Taylor D. Sparks,Alán Aspuru-Guzik +12 more
TL;DR: Review of low-cost self-driving laboratories in chemistry and materials science proposes the concept of frugal twins for physical experiments.
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The Future of Material Scientists in an Age of Artificial Intelligence.
Ayman Maqsood,Chen Chen,T. J. Jacobsson +2 more
TL;DR: The future of material science in an age of AI holds substantial potential for accelerating research efforts through AI-powered tools like data-fitting, experimental design, and hypothesis formulation.
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NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science
TL;DR: NIMS-OS as discussed by the authors is a Python library created to realize a closed loop of robotic experiments and artificial intelligence (AI) without human intervention for automated materials exploration using various combinations of modules to operate autonomously.
9
Review of Low-cost Self-driving Laboratories: The "Frugal Twin" Concept
Stanley Lo,Sterling G. Baird,Joshua Schrier,Ben Blaiszik,Sergei V. Kalinin,Helen Tran,Taylor D. Sparks,Alán Aspuru‐Guzik +7 more
- 08 Sep 2023
TL;DR: Review of low-cost self-driving laboratories ("frugal twins") proposes the concept of low-cost surrogates of high-cost research experiments. They provide hands-on experience, test beds for software prototyping, and low barrier to entry. However, there is room for improvement in hardware/software modularity, purpose-built design, and software.
3
Bayesian optimization of glycopolymer structures for the interaction with cholera toxin B subunit
Masashi Nagao,Osuke Nakahara,Xin Zhou,Hikaru Matsumoto,Yoshiko Miura +4 more
TL;DR: Sure, here is the TLDR: Bayesian optimization was used to determine the optimal structure of glycopolymers for the interaction with cholera toxin B subunit.
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