An open experimental database for exploring inorganic materials

Zakutayev, Andriy; Wunder, Nick; Schwarting, Marcus; Perkins, John D.; White, Robert R.; Munch, Kristin; Tumas, William; Phillips, Caleb

doi:10.1038/sdata.2018.53

Cited by 151 publications

(127 citation statements)

References 35 publications

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“…A large fraction of experimental data are only available in journal publications, though recent successes in text mining offer a potential solution to this conundrum . Finally, major efforts are underway in high‐throughput/combinatorial experiments that can generate large experimental materials database with diverse properties …”

Section: Data Collectionmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

399

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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Section: Data Collectionmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

399

281

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“…27 Experimental and computational characterization of framework materials has, so far, taken place in a very step-by-step fashion, where a few materials are characterized in depth in each study. 18,57,58 Based on the knowledge gained in this study, we now look to apply high-throughput screening methods to larger databases of materials, 59 in order to accelerate discovery of mechanical metamaterials based not on their complete elastic characterization, but on descriptors such as their structural motifs, composition, topology, etc.…”

Section: Discussionmentioning

confidence: 99%

Systematic Exploration of the Mechanical Properties of 13,621 Inorganic Compounds

Chibani¹,

Coudert

2019

Preprint

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In order to better understand the mechanical properties of crystalline materials, we performed a large-scale exploration of the elastic properties of 13,621 crystals from the Materials Project database, including both experimentally synthesized and hypothetical structures. We studied both their average (isotropic) behavior, as well as the anisotropy of the elastic properties: bulk modulus, shear modulus, Young’s modulus, Poisson’s ratio, and linear compressibility. We show that general mechanical trends, which hold for isotropic (noncrystalline) materials at the macroscopic scale, also apply “on average” for crystals. Further, we highlight the importance of elastic anisotropy and the role of mechanical stability as playing key roles in the experimental feasibility of hypothetical compounds. We also quantify the frequency of occurrence of rare anomalous mechanical properties: 3% of the crystals feature negative linear compressibility, and only 0.3% have complete auxeticity.

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“…Whereas the targets are activity, selectivity, and stability, the features can be any physical property or even synthesis methods. Commonly, features are based on the structure and composition of the material …”

Section: Machine Learning Conceptsmentioning

confidence: 99%

Machine Learning for Computational Heterogeneous Catalysis

et al. 2019

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Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis.

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An open experimental database for exploring inorganic materials

Cited by 151 publications

References 35 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Systematic Exploration of the Mechanical Properties of 13,621 Inorganic Compounds

Machine Learning for Computational Heterogeneous Catalysis

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