Toward “On‐Demand” Materials Synthesis and Scientific Discovery through Intelligent Robots

Li, Jiagen; Tu, Yuxiao; Liu, Rulin; Lu, Yu; Zhu, Xi

doi:10.1002/advs.201901957

Cited by 49 publications

(47 citation statements)

References 43 publications

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“…1. Robotic automation, cloud servers, sensing devices, and communication tools are indispensable, and the central platform-MAOSIC (upgraded from our previous system MAOS 27 )-managed all issues. To complete the complex experimental tasks, five functional modules were integrated within MAOSIC, including (a) a human-machine interaction, (b) a hardware control interface, (c) an analysis module, (d) optimization modules and (e) a cloud control interface (see Supplementary Method 1 for the detailed architectures).…”

Section: Resultsmentioning

confidence: 99%

Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab

et al. 2020

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We constructed an intelligent cloud lab that integrates lab automation with cloud servers and artificial intelligence (AI) to detect chirality in perovskites. Driven by the materials acceleration operating system in cloud (MAOSIC) platform, on-demand experimental design by remote users was enabled in this cloud lab. By employing artificial intelligence of things (AIoT) technology, synthesis, characterization, and parameter optimization can be autonomously achieved. Through the remote collaboration of researchers, optically active inorganic perovskite nanocrystals (IPNCs) were first synthesized with temperature-dependent circular dichroism (CD) and inversion control. The inter-structure (structural patterns) and intrastructure (screw dislocations) dual-pattern-induced mechanisms detected by MAOSIC were comprehensively investigated, and offline theoretical analysis revealed the thermodynamic mechanism inside the materials. This self-driving cloud lab enables efficient and reliable collaborations across the world, reduces the setup costs of in-house facilities, combines offline theoretic analysis, and is practical for accelerating the speed of material discovery.

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Section: Resultsmentioning

confidence: 99%

Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab

et al. 2020

Self Cite

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“…Investment is needed in the software infrastructure for materials AE. Atinary (formerly Che-mOS), 159 ESCALATE, 94 LabMate.ML, 160 MAOS, 161 BlueSky, 162 and ARES OS 28 are examples of such efforts. However, the broader range of materials, modeling Review software, and experimental hardware will require further investment into software.…”

Section: Investments In Software Infrastructurementioning

confidence: 99%

Autonomous experimentation systems for materials development: A community perspective

Stach

DeCost

Kusne

et al. 2021

Matter

212

138

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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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“…We usually need here much larger data; therefore, more and more often, robots are replacing chemists. This can be observed for chemical compounds [ 98 ] or materials [ 99 ]. However, a question is how general these methods will appear.…”

Section: In Silico Design Of Heterogenous Catalystsmentioning

confidence: 99%

Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem

Lach

Zhdan

Smoliński

et al. 2021

IJMS

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(1) Background: Properties and descriptors are two forms of molecular in silico representations. Properties can be further divided into functional, e.g., catalyst or drug activity, and material, e.g., X-ray crystal data. Millions of real measured functional property records are available for drugs or drug candidates in online databases. In contrast, there is not a single database that registers a real conversion, TON or TOF data for catalysts. All of the data are molecular descriptors or material properties, which are mainly of a calculation origin. (2) Results: Here, we explain the reason for this. We reviewed the data handling and sharing problems in the design and discovery of catalyst candidates particularly, material informatics and catalyst design, structural coding, data collection and validation, infrastructure for catalyst design and the online databases for catalyst design. (3) Conclusions: Material design requires a property prediction step. This can only be achieved based on the registered real property measurement. In reality, in catalyst design and discovery, we can observe either a severe functional property deficit or even property famine.

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Toward “On‐Demand” Materials Synthesis and Scientific Discovery through Intelligent Robots

Cited by 49 publications

References 43 publications

Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab

Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab

Autonomous experimentation systems for materials development: A community perspective

Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem

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