Materials informatics platform with three dimensional structures, workflow and thermoelectric applications

Since the proposal of the “Materials Genome Initiative”, several material databases have emerged and advanced many materials fields. In this work, we present the Materials Informatics Platform with Three-Dimensional Structures (MIP-3d). More than 80,000 structural entries, mainly from the inorganic crystal structural database, are included in MIP-3d. Density functional theory calculations are carried out for over 30,000 entries in the database, which contain the relaxed crystal structures, density of states, and band structures. The calculation of the equations of state and sound velocities is performed for over 12,000 entries. Notably, for entries with band gap values larger than 0.3 eV, the band degeneracies for the valence band maxima and the conduction band minima are analysed. The electrical transport properties for approximately 4,400 entries are also calculated and presented in MIP-3d under the constant electron-phonon coupling approximation. The calculations of the band degeneracies and electrical transport properties make MIP-3d a database specifically designed for thermoelectric applications.

An infrastructure with user-centered presentation data model for integrated management of materials data and services

With scientific research in materials science becoming more data intensive and collaborative after the announcement of the Materials Genome Initiative, the need for modern data infrastructures that facilitate the sharing of materials data and analysis tools is compelling in the materials community. In this paper, we describe the challenges of developing such infrastructure and introduce an emerging architecture with high usability. We call this architecture the Materials Genome Engineering Databases (MGED). MGED provides cloud-hosted services with features to simplify the process of collecting datasets from diverse data providers, unify data representation forms with user-centered presentation data model, and accelerate data discovery with advanced search capabilities. MGED also provides a standard service management framework to enable finding and sharing of tools for analyzing and processing data. We describe MGED’s design, current status, and how MGED supports integrated management of shared data and services.

Structural hierarchy from wavelet zoom and invariant construction

During the past decade there have seen substantial progress being made on materials genome related research. However, coupling mechanisms across multi-scale microstructure and resulting consequences on property and performance of materials remain unsolved problems. Structural hierarchy, which was a concept developed but not quantitatively fulfilled in 1970s, is referred to as microstructure genome here and pinpoints the key enabler for materials genome engineering. Latest progress in deep learning for image recognition and understanding the underlying mathematical mechanisms have revealed the pivotal roles that directional wavelets and invariants play. Hierarchical invariants constructed by a wavelet system can provide an inherent descriptor for microstructure genome.

Designing high-performance hypergolic propellants based on materials genome

A new generation of rocket propellants for deep space exploration, ionic liquid propellants, with long endurance and high stability, is attracting more and more attention. However, a major defect of ionic liquid propellants that restricts their application is the inadequate hypergolic reactivity between the fuel and the oxidant, and this defect results in local burnout and accidental explosions during the launch process. We propose a visualization model to show the features of structure, density, thermal stability, and hypergolic activity for estimating propellant performances and their application abilities. This propellant materials genome and visualization model greatly improves the efficiency and quality of developing high-performance propellants, which benefits the discovery of new advanced functional molecules in the field of energetic materials.

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov.

Active learning for the power factor prediction in diamond-like thermoelectric materials

The Materials Genome Initiative requires the crossing of material calculations, machine learning, and experiments to accelerate the material development process. In recent years, data-based methods have been applied to the thermoelectric field, mostly on the transport properties. In this work, we combined data-driven machine learning and first-principles automated calculations into an active learning loop, in order to predict the p-type power factors (PFs) of diamond-like pnictides and chalcogenides. Our active learning loop contains two procedures (1) based on a high-throughput theoretical database, machine learning methods are employed to select potential candidates and (2) computational verification is applied to these candidates about their transport properties. The verification data will be added into the database to improve the extrapolation abilities of the machine learning models. Different strategies of selecting candidates have been tested, finally the Gradient Boosting Regression model of Query by Committee strategy has the highest extrapolation accuracy (the Pearson R = 0.95 on untrained systems). Based on the prediction from the machine learning models, binary pnictides, vacancy, and small atom-containing chalcogenides are predicted to have large PFs. The bonding analysis reveals that the alterations of anionic bonding networks due to small atoms are beneficial to the PFs in these compounds.