Using simulation to accelerate autonomous experimentation: A case study using mechanics.
Aldair E. Gongora,Kelsey L. Snapp,Emily Whiting,Patrick Riley,Kristofer G. Reyes,Elise F. Morgan,Keith A. Brown +6 more
TLDR
In this article, the authors investigate whether imperfect data from simulation can accelerate autonomous experimentation using a case study on the mechanics of additively manufactured structures, and highlight multiple ways that simulation can improve AE through transfer learning.About:
This article is published in iScience.The article was published on 2021-03-02 and is currently open access. It has received 28 citations till now.read more
Citations
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Journal ArticleDOI
Autonomous experimentation systems for materials development: A community perspective
Eric A. Stach,Brian L. DeCost,A. Gilad Kusne,A. Gilad Kusne,Jason R. Hattrick-Simpers,Keith A. Brown,Kristofer G. Reyes,Joshua Schrier,Simon J. L. Billinge,Simon J. L. Billinge,Tonio Buonassisi,Ian Foster,Ian Foster,Carla P. Gomes,John M. Gregoire,Apurva Mehta,Joseph Montoya,Elsa Olivetti,Chiwoo Park,Eli Rotenberg,Semion K. Saikin,Sylvia Smullin,Valentin Stanev,Benji Maruyama +23 more
TL;DR: In this paper, the authors discuss the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm and outline the current status, barriers and needed investments, culminating with a vision for the path forward.
Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains
Qiaohao Liang,Aldair E. Gongora,Zekun Ren,Armi Tiihonen,Zhe Liu,Shijing Sun,James R. Deneault,Daniil Bash,Flore Mekki-Berrada,Saif A. Khan,Kedar Hippalgaonkar,Benji Maruyama,Keith A. Brown,John W. Fisher,Tonio Buonassisi +14 more
TL;DR: In this paper, the authors evaluate the performance of active learning algorithms such as Bayesian optimization (BO) for general materials optimization and find that for surrogate model selection, Gaussian Process (GP) with anisotropic kernels (automatic relevance detection, ARD) and Random Forests (RF) have comparable performance and both outperform the commonly used GP without ARD.
Journal ArticleDOI
Machine learning for high-throughput experimental exploration of metal halide perovskites
TL;DR: In this paper, the authors provide an overview of the state of the art in automated metal halide perovskites (MHPs) synthesis and existing methods for navigating multicomponent compositional space.
Journal ArticleDOI
The rise of self-driving labs in chemical and materials sciences
TL;DR: Self-driving Lab (SDL) as discussed by the authors is a machine-learning-assisted modular experimental platform that iteratively operates a series of experiments selected by the machine learning algorithm to achieve a user-defined objective.
Journal ArticleDOI
Autonomous chemical science and engineering enabled by self-driving laboratories
TL;DR: In this paper , the authors discuss different elements of a self-driving lab, and present recent efforts in autonomous reaction modeling and optimization, which can realize the full potential of autonomous chemical science and engineering to accelerate the discovery and development of advanced materials and molecules.
References
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Journal ArticleDOI
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
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Bioinspired structural materials
TL;DR: The common design motifs of a range of natural structural materials are reviewed, and the difficulties associated with the design and fabrication of synthetic structures that mimic the structural and mechanical characteristics of their natural counterparts are discussed.
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Topology optimization approaches: A comparative review
Ole Sigmund,Kurt Maute +1 more
TL;DR: An overview, comparison and critical review of the different approaches to topology optimization, their strengths, weaknesses, similarities and dissimilarities and suggests guidelines for future research.
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Materials become insensitive to flaws at nanoscale: lessons from nature.
TL;DR: It is shown that the nanocomposites in nature exhibit a generic mechanical structure in which the nanometer size of mineral particles is selected to ensure optimum strength and maximum tolerance of flaws (robustness) and the widely used engineering concept of stress concentration at flaws is no longer valid for nanomaterial design.
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Functional genomic hypothesis generation and experimentation by a robot scientist
Ross D. King,Kenneth E. Whelan,Ffion M. Jones,Philip G. K. Reiser,Christopher H. Bryant,Stephen Muggleton,Douglas B. Kell,Stephen G. Oliver +7 more
TL;DR: A physically implemented robotic system that applies techniques from artificial intelligence to carry out cycles of scientific experimentation and shows that an intelligent experiment selection strategy is competitive with human performance and significantly outperforms, with a cost decrease of 3-fold and 100-fold, both cheapest and random-experiment selection.
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