Digital Transformation in Materials Science: A Paradigm Change in Material's Development

Kimmig, Julian; Zechel, Stefan; Schubert, Ulrich S.

doi:10.1002/adma.202004940

Cited by 40 publications

(37 citation statements)

References 84 publications

(103 reference statements)

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“…Digital representation of data retrieved during experiments literally transformed the ways of sharing and exchanging results, supporting information, and software codes by enabling representation and quantification of the discovered material structures. Digitalization and automation have great potential in the field of material synthesis [13] [14], automated flow nanomaterial synthesis platforms [15], and problem-oriented specific digital microfluidic devices [16], which will be detailed in the next section.…”

Section: Artificial Intelligence and Machine Learning Role In Materiamentioning

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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Section: Artificial Intelligence and Machine Learning Role In Materiamentioning

confidence: 99%

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

et al. 2021

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“…At a fast glance, the chemist’s skills are extremely individual, and a kind of art and experience, and contradict with formalized and systematic AI, automation, and robotics principles. Despite this fact and recognizing synthesis as a holistic system, we should admit the growing influence and role of machine-assisted synthesis [ 74 ], digital transformation [ 75 ], and the inverse design paradigm [ 76 ] in the mindset of experimenters.…”

Section: Data-centric Research Strategymentioning

confidence: 99%

Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

et al. 2021

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Artificial intelligence (AI) approaches continue to spread in almost every research and technology branch. However, a simple adaptation of AI methods and algorithms successfully exploited in one area to another field may face unexpected problems. Accelerating the discovery of new functional materials in chemical self-driving laboratories has an essential dependence on previous experimenters’ experience. Self-driving laboratories help automate and intellectualize processes involved in discovering nanomaterials with required parameters that are difficult to transfer to AI-driven systems straightforwardly. It is not easy to find a suitable design method for self-driving laboratory implementation. In this case, the most appropriate way to implement is by creating and customizing a specific adaptive digital-centric automated laboratory with a data fusion approach that can reproduce a real experimenter’s behavior. This paper analyzes the workflow of autonomous experimentation in the self-driving laboratory and distinguishes the core structure of such a laboratory, including sensing technologies. We propose a novel data-centric research strategy and multilevel data flow architecture for self-driving laboratories with the autonomous discovery of new functional nanomaterials.

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“…1,2 In materials science, discovery of new materials, optimization of processes, and enhancement of performances have been achieved by datadriven methods. [3][4][5][6][7][8][9][10][11][12][13][14][15] In chemistry, new functional molecules and catalysts have been found using ML. Combination of ML and robotic equipments further accelerates discovery.…”

Section: Introductionmentioning

confidence: 99%

Sparse modeling for small data: case studies in controlled synthesis of 2D materials

et al. 2022

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Digital Transformation in Materials Science: A Paradigm Change in Material's Development

Cited by 40 publications

References 84 publications

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

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

Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials

Sparse modeling for small data: case studies in controlled synthesis of 2D materials

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