Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design. © 2013 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.

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Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

https://dc.engconfintl.org/cgi/viewcontent.cgi?article=1032&context=superalloys_ii

A critical review of high entropy alloys and related concepts

Nanoparticles: Properties, applications and toxicities

Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.

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Machine learning for molecular and materials science.

High-throughput density functional theory (HT DFT) is fast becoming a powerful tool for accelerating materials design and discovery by the amassing tens and even hundreds of thousands of DFT calculations in large databases. Complex materials problems can be approached much more efficiently and broadly through the sheer quantity of structures and chemistries available in such databases. Our HT DFT database, the Open Quantum Materials Database (OQMD), contains over 200,000 DFT calculated crystal structures and will be freely available for public use at http://oqmd.org
 . In this review, we describe the OQMD and its use in five materials problems, spanning a wide range of applications and materials types: (I) Li-air battery combination catalyst/electrodes, (II) Li-ion battery anodes, (III) Li-ion battery cathode coatings reactive with HF, (IV) Mg-alloy long-period stacking ordered (LPSO) strengthening precipitates, and (V) training a machine learning model to predict new stable ternary compounds.

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

The Open Quantum Materials Database (OQMD) is a high-throughput database currently consisting of nearly 300,000 density functional theory (DFT) total energy calculations of compounds from the Inorganic Crystal Structure Database (ICSD) and decorations of commonly occurring crystal structures. To maximise the impact of these data, the entire database is being made available, without restrictions, at www.oqmd.org/download
 . In this paper, we outline the structure and contents of the database, and then use it to evaluate the accuracy of the calculations therein by comparing DFT predictions with experimental measurements for the stability of all elemental ground-state structures and 1,670 experimental formation energies of compounds. This represents the largest comparison between DFT and experimental formation energies to date. The apparent mean absolute error between experimental measurements and our calculations is 0.096 eV/atom. In order to estimate how much error to attribute to the DFT calculations, we also examine deviation between different experimental measurements themselves where multiple sources are available, and find a surprisingly large mean absolute error of 0.082 eV/atom. Hence, we suggest that a significant fraction of the error between DFT and experimental formation energies may be attributed to experimental uncertainties. Finally, we evaluate the stability of compounds in the OQMD (including compounds obtained from the ICSD as well as hypothetical structures), which allows us to predict the existence of ~3,200 new compounds that have not been experimentally characterised and uncover trends in material discovery, based on historical data available within the ICSD. Researchers in the USA and Germany introduce a database of over 300,000 calculations detailing the electronic structure and stability of inorganic materials. Chris Wolverton and co-workers from Northwestern University and the Leibniz Institute for Information Infrastructure describe the structure of the Open Quantum Materials Database—a catalog storing information about the electronic properties of a significant fraction of the known crystalline solids determined using density functional theory calculations. Density functional theory is a powerful computational technique that uses quantum mechanics to determine the lowest energy state of the electrons travelling through a lattice of atoms. The researchers verified the accuracy of the calculations by comparing them to experimental results on 1,670 crystals. The database is freely available to scientists, enabling them to design and predict the properties of as yet unrealised materials.

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The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

Typically, computational screens for new materials sharply constrain the compositional search space, structural search space, or both, for the sake of tractability. To lift these constraints, we construct a machine learning model from a database of thousands of density functional theory (DFT) calculations. The resulting model can predict the thermodynamic stability of arbitrary compositions without any other input and with six orders of magnitude less computer time than DFT. We use this model to scan roughly 1.6 million candidate compositions for novel ternary compounds (${A}_{x}{B}_{y}{C}_{z}$), and predict 4500 new stable materials. Our method can be readily applied to other descriptors of interest to accelerate domain-specific materials discovery.

Combinatorial screening for new materials in unconstrained composition space with machine learning

The use of hydrogen as fuel is a promising avenue to aid in the reduction of greenhouse effect gases released in the atmosphere. In this work, we present a high-throughput density functional theory (HT-DFT) study of 5,329 cubic and distorted perovskite ABO3 compounds to screen for thermodynamically favorable two-step thermochemical water splitting (TWS) materials. From a data set of more than 11,000 calculations, we screened materials based on the following: (a) thermodynamic stability and (b) oxygen vacancy formation energy that allow favorable TWS. From our screening strategy, we identify 139 materials as potential new candidates for TWS application. Several of these compounds, such as CeCoO3 and BiVO3, have not been experimentally explored yet for TWS and present promising avenues for further research. We show that taking into consideration all phases present in the A–B–O ternary phase, as opposed to only calculating the formation energy of a compound, is crucial to assess correctly the stability of a ...

James E. Saal

Papers

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

Combinatorial screening for new materials in unconstrained composition space with machine learning

High-Throughput Computational Screening of Perovskites for Thermochemical Water Splitting Applications

Enthalpies of formation of magnesium compounds from first-principles calculations