Data-driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning—typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high-throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data-driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data-driven materials science, building from the early evolution of open science to the rapid expansion of materials data frastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.201900808

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

The Materials Genome Initiative (MGI) advanced a new paradigm for materials discovery and design, namely that the pace of new materials deployment could be accelerated through complementary efforts in theory, computation, and experiment. Along with numerous successes, new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI. In May 2017, the National Science Foundation sponsored the workshop “Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation, Experiment, and Theory: Opening New Frontiers” to review accomplishments that emerged from investments in science and infrastructure under the MGI, identify scientific opportunities in this new environment, examine how to effectively utilize new materials innovation infrastructure, and discuss challenges in achieving accelerated materials research through the seamless integration of experiment, computation, and theory. This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.

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New frontiers for the materials genome initiative

Data are a crucial raw material of this century. The amount of data that have been created in materials science thus far and that continues to be created every day is immense. Without a proper infrastructure that allows for collecting and sharing data, the envisioned success of big data-driven materials science will be hampered. For the field of computational materials science, the NOMAD (Novel Materials Discovery) Center of Excellence (CoE) has changed the scientific culture toward comprehensive and findable, accessible, interoperable, and reusable (FAIR) data, opening new avenues for mining materials science big data. Novel data-analytics concepts and tools turn data into knowledge and help in the prediction of new materials and in the identification of new properties of already known materials.

NOMAD: The FAIR concept for big data-driven materials science

Advances in machine learning (ML) are making a large impact in many fields, including: artificial intelligence, materials science, and chemical engineering. Generally, ML tools learn from data to find insights or make fast predictions of target properties. Recently, ML is also greatly influencing heterogeneous catalysis research due to the availability of ML (e.g., Python Scikit-learn, TensorFlow) and workflow management tools (e.g., ASE, Atomate), the growing amount of data in materials databases (e.g., Novel Materials Discovery Laboratory, Citrination, Materials Project, CatApp), and algorithmic improvements. New catalysts are needed for sustainable chemical production, alternative energy, and pollution mitigation applications to meet the demands of our world’s rising population. It is a challenging endeavor, however, to make novel heterogeneous catalysts with good performance (i.e., stable, active, selective) because their performance depends on many properties: composition, support, surface termination, particle size, particle morphology, and atomic coordination environment. Additionally, the properties of heterogeneous catalysts can change under reaction conditions through various phenomena such as Ostwald ripening, particle disintegration, surface oxidation, and surface reconstruction. Many heterogeneous catalyst structures are disordered or amorphous in their active state, which further complicates their atomic-level characterization by modeling and experiment. Computational modeling using quantum mechanical (QM) methods such as density functional theory (DFT) can accelerate catalyst screening by enabling rapid prototyping and revealing active sites and structure-activity relations. The high computational cost of QM methods, however, limits the range of catalyst spaces that can be examined. Recent progress in merging ML with QM modeling and experiments promises to drive forward rational catalyst design. Therefore, it is timely to highlight the ability of ML tools to accelerate

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Machine learning for heterogeneous catalyst design and discovery

TheNovelMaterials Discovery (NOMAD) Laboratory is a user-driven platform for sharing and exploiting computationalmaterials science data. It accounts for the various aspects of data being a crucial rawmaterial andmost relevant to acceleratematerials research and engineering. NOMAD, with theNOMADRepository, and its code-independent and normalized form, theNOMADArchive, comprises theworldwide largest data collection of thisfield. Based on itsfindable accessible, interoperable, reusable data infrastructure, various services are offered, comprising advanced visualization, theNOMADEncyclopedia, and artificial-intelligence tools. The latter are realized in the NOMADAnalytics Toolkit. Prerequisite for all this is theNOMADmetadata, a unique and thorough description of the data, that are produced by all important computer codes of the community. Uploaded data are tagged by a persistent identifier, and users can also request a digital object identifier tomake data citable. Developments and advancements of parsers andmetadata are organized jointly with users and code developers. In this work, we review theNOMADconcept and implementation, highlight its orthogonality to and synergistic interplaywith other data collections, and provide an outlook regarding ongoing and future developments.

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The NOMAD Laboratory: From Data Sharing to Artificial Intelligence

With big-data driven materials research, the new paradigm of materials science, sharing and wide accessibility of data are becoming crucial aspects. Obviously, a prerequisite for data exchange and big-data analytics is standardization, which means using consistent and unique conventions for, e.g., units, zero base lines, and file formats. There are two main strategies to achieve this goal. One accepts the heterogeneous nature of the community, which comprises scientists from physics, chemistry, bio-physics, and materials science, by complying with the diverse ecosystem of computer codes and thus develops “converters” for the input and output files of all important codes. These converters then translate the data of each code into a standardized, code-independent format. The other strategy is to provide standardized open libraries that code developers can adopt for shaping their inputs, outputs, and restart files, directly into the same code-independent format. In this perspective paper, we present both strategies and argue that they can and should be regarded as complementary, if not even synergetic. The represented appropriate format and conventions were agreed upon by two teams, the Electronic Structure Library (ESL) of the European Center for Atomic and Molecular Computations (CECAM) and the NOvel MAterials Discovery (NOMAD) Laboratory, a European Centre of Excellence (CoE). A key element of this work is the definition of hierarchical metadata describing state-of-the-art electronic-structure calculations.

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Fawzi Roberto Mohamed

Papers

Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats