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RESEARCH PAPER · npj Computational Materials · 2019

New frontiers for the materials genome initiative

Juan Pablo, Nicholas E. Jackson, Michael A. Webb, Long‐Qing Chen, Joel E. Moore, Dane D. Morgan, Ryan M. Jacobs, Tresa M. Pollock, Darrell G. Schlom, Eric S. Toberer, James G. Analytis, Ismaïla Dabo, Dean M. DeLongchamp, Gregory A. Fiete, Gregory M. Grason, Geoffroy Hautier, Yifei Mo, Krishna Rajan, Evan J. Reed, Efrain E. Rodriguez, Vladan Stevanović, Jin Suntivich, Katsuyo S. Thornton, Ji‐Cheng Zhao

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New frontiers for the materials genome initiativeOriginalNanoinformatics, and the big challenges for the science of small things92Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment41Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics185High throughput experiment assisted discovery of new Ni-base superalloys72Crystallographic prediction from diffraction and chemistry data for higher throughput classification using machine learning49Navigating the design space of inorganic materials synthesis using statistical methods and machine learning43The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research78A Design-to-Device Pipeline for Data-Driven Materials Discovery114A brief review of data-driven ICME for intelligently discovering advanced structural metal materials: Insight into atomic and electronic building blocks36A data-driven framework to predict the morphology of interfacial Cu6Sn5 IMC in SAC/Cu system during laser soldering56Deformation behavior and amorphization in icosahedral boron-rich ceramics52Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks112Trends in 3D Printing Processes for Biomedical Field: Opportunities and Challenges210Opportunities and Challenges for Machine Learning in Materials Science522High-throughput determination of high-quality interdiffusion coefficients in metallic solids: a review60Modeling the dielectric constants of crystals using machine learning69Polymer Nanocomposite Data: Curation, Frameworks, Access, and Potential for Discovery and Design49Machine learning prediction of self-diffusion in Lennard-Jones fluids54Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN61A database of battery materials auto-generated using ChemDataExtractor162Two-dimensional hybrid organic–inorganic perovskites as emergent ferroelectric materials48Towards artificial molecular factories from framework-embedded molecular machines160Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends146Prediction of tensile strength of polymer carbon nanotube composites using practical machine learning method97High-throughput design of magnetic materials52Solid state chemistry for developing better metal-ion batteries259MAISE: Construction of neural network interatomic models and evolutionary structure optimization56Integrating data mining and machine learning to discover high-strength ductile titanium alloys188Machine learning to predict properties of fresh and hardened alkali-activated concrete189Automation and data-driven design of polymer therapeutics104Advances in thermal conductivity for energy applications: a review86Characterising soft matter using machine 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