Machine-Learning-Accelerated Perovskite Crystallization

Kirman, Jeffrey; Johnston, Andrew; Kuntz, D.A.; Askerka, Mikhail; Gao, Yuan; Todorovič, P.; Ma, Dávid; Privé, G.G.; Sargent, Edward H.

doi:10.1016/j.matt.2020.02.012

Cited by 112 publications

(85 citation statements)

References 28 publications

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“…Machine learning combined with high‐throughput experimentation will accelerate the discovery and development of new MHPs and might free researchers from trial and error. [ 313 ] Mass‐production methods of low‐dimensional MHPs for large‐scale industrial purposes are also needed. For example, the quality, cost, reliability, and stability of metal contacts in PD devices must be more carefully investigated to ensure efficient charge transport and achieve better device performance.…”

Section: Discussionmentioning

confidence: 99%

Low‐Dimensional Metal Halide Perovskite Photodetectors

et al. 2020

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Metal halide perovskites (MHPs) have been a hot research topic due to their facile synthesis, excellent optical and optoelectronic properties, and record‐breaking efficiency of corresponding optoelectronic devices. Nowadays, the development of miniaturized high‐performance photodetectors (PDs) has been fueling the demand for novel photoactive materials, among which low‐dimensional MHPs have attracted burgeoning research interest. In this report, the synthesis, properties, photodetection performance, and stability of low‐dimensional MHPs, including 0D, 1D, 2D layered and nonlayered nanostructures, as well as their heterostructures are reviewed. Recent advances in the synthesis approaches of low‐dimensional MHPs are summarized and the key concepts for understanding the optical and optoelectronic properties related to the PD applications of low‐dimensional MHPs are introduced. More importantly, recent progress in novel PDs based on low‐dimensional MHPs is presented, and strategies for improving the performance and stability of perovskite PDs are highlighted. By discussing recent advances, strategies, and existing challenges, this progress report provides perspectives on low‐dimensional MHP‐based PDs in the future.

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Section: Discussionmentioning

confidence: 99%

Low‐Dimensional Metal Halide Perovskite Photodetectors

et al. 2020

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“…Kirman et al employed optical observation of crystallization to identify novel perovskites. 92 HT experiments were made possible by using instrumentation developed for protein crystallography studies. ML was applied to both optically analyze samples to evaluate crystallization and to build a predictive model of whether samples would crystallize.…”

Section: Developments In Characterization and Analytical Methods In Efforts Toward Aementioning

confidence: 99%

Autonomous experimentation systems for materials development: A community perspective

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Kusne

et al. 2021

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Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.

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“…However, the growth of single crystals is a challenging task. Jeffrey Kirman and coauthors applied machine learning and improved robotic synthetics to optimize the antisolvent vapor-assisted crystallization method [ 33 ]. In the first step of the experiment, the authors trained the ANN to recognize single crystals in the product and, based on this observation, marked the synthesis conditions as optimal or failed.…”

Section: Ai Applications In the Synthesis: From High-throughput Scmentioning

confidence: 99%

Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions

et al. 2021

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Innovations often play an essential role in the acceleration of the new functional materials discovery. The success and applicability of the synthesis results with new chemical compounds and materials largely depend on the previous experience of the researcher himself and the modernity of the equipment used in the laboratory. Artificial intelligence (AI) technologies are the next step in developing the solution for practical problems in science, including the development of new materials. Those technologies go broadly beyond the borders of a computer science branch and give new insights and practical possibilities within the far areas of expertise and chemistry applications. One of the attractive challenges is an automated new functional material synthesis driven by AI. However, while having many years of hands-on experience, chemistry specialists have a vague picture of AI. To strengthen and underline AI’s role in materials discovery, a short introduction is given to the essential technologies, and the machine learning process is explained. After this review, this review summarizes the recent studies of new strategies that help automate and accelerate the development of new functional materials. Moreover, automatized laboratories’ self-driving cycle could benefit from using AI algorithms to optimize new functional nanomaterials’ synthetic routes. Despite the fact that such technologies will shape material science in the nearest future, we note the intelligent use of algorithms and automation is required for novel discoveries.

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Machine-Learning-Accelerated Perovskite Crystallization

Cited by 112 publications

References 28 publications

Low‐Dimensional Metal Halide Perovskite Photodetectors

Low‐Dimensional Metal Halide Perovskite Photodetectors

Autonomous experimentation systems for materials development: A community perspective

Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions

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