Data‐Driven Materials Science: Status, Challenges, and Perspectives

This two‐part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this second part, we reflect on a selection of exemplary studies. It is increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated systems have yet to “discover” despite being incredibly useful as laboratory assistants. We must carefully consider how they have been and can be applied to future problems of chemical discovery in order to effectively design and interact with future autonomous platforms. The majority of this Review defines a large set of open research directions, including improving our ability to work with complex data, build empirical models, automate both physical and computational experiments for validation, select experiments, and evaluate whether we are making progress towards the ultimate goal of autonomous discovery. Addressing these practical and methodological challenges will greatly advance the extent to which autonomous systems can make meaningful discoveries.

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“…Additional databases related to materials science can be found in Ref. [37] and [38]. Some related to drug discovery are contained in Refs.…”

Section: Challenges and Trendsmentioning

confidence: 99%

Autonomous Discovery in the Chemical Sciences Part II: Outlook

2020

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“…New trends in the methods of storing catalysis data beyond publications in classical journals will also contribute to a change in the paradigm and will help to improve the public access to standardized data, see for example the advancements for computationally generated data in material sciences [123], surface reactions [124], but also experimental data [125].…”

Section: Discussionmentioning

confidence: 99%

Towards Experimental Handbooks in Catalysis

et al. 2020

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The “Seven Pillars” of oxidation catalysis proposed by Robert K. Grasselli represent an early example of phenomenological descriptors in the field of heterogeneous catalysis. Major advances in the theoretical description of catalytic reactions have been achieved in recent years and new catalysts are predicted today by using computational methods. To tackle the immense complexity of high-performance systems in reactions where selectivity is a major issue, analysis of scientific data by artificial intelligence and data science provides new opportunities for achieving improved understanding. Modern data analytics require data of highest quality and sufficient diversity. Existing data, however, frequently do not comply with these constraints. Therefore, new concepts of data generation and management are needed. Herein we present a basic approach in defining best practice procedures of measuring consistent data sets in heterogeneous catalysis using “handbooks”. Selective oxidation of short-chain alkanes over mixed metal oxide catalysts was selected as an example.

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“…[59] There are also many repositories for experimental and computational properties of materials,w hich have facilitated the construction of empirical models to predict new material performance. [60,61] Gil et al [62] discussed the utility of AI techniques in searching and synthesizing large amounts of information as part of "discovery informatics". [57,63,64] Even now,a ne normous amount of untapped information remains housed in laboratory notebooks and journal articles.F or such information to be directly usable,s omeone must undertake the challenge of compiling the data into an accessible,u serfriendly format and overcome any intellectual property restrictions.I mage and natural language processing techniques can make this task less burdensome;t hus there is increasing interest in applying such techniques to the chemical sciences.…”

Section: Enabling Factorsmentioning

confidence: 99%

Autonomous Discovery in the Chemical Sciences Part I: Progress

2020

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This two‐part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this first part, we describe a classification for discoveries of physical matter (molecules, materials, devices), processes, and models and how they are unified as search problems. We then introduce a set of questions and considerations relevant to assessing the extent of autonomy. Finally, we describe many case studies of discoveries accelerated by or resulting from computer assistance and automation from the domains of synthetic chemistry, drug discovery, inorganic chemistry, and materials science. These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modeling. Part two reflects on these case studies and identifies a set of open challenges for the field.

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Data‐Driven Materials Science: Status, Challenges, and Perspectives

Cited by 497 publications

References 232 publications

Autonomous Discovery in the Chemical Sciences Part II: Outlook

Autonomous Discovery in the Chemical Sciences Part II: Outlook

Towards Experimental Handbooks in Catalysis

Autonomous Discovery in the Chemical Sciences Part I: Progress

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