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RESEARCH PAPER · Journal of Physics D Applied Physics · 2018

The 2019 materials by design roadmap

Kirstin Alberi, Marco Buongiorno Nardelli, Andriy Zakutayev, Luboš Mitáš, Stefano Curtarolo, Anubhav Jain, Marco Fornari, Nicola Marzari, Ichiro Takeuchi, Martin L. Green, Mercouri G. Kanatzidis, Mike F Toney, Sergiy I. Butenko, Bryce Meredig, Stephan Lany, Ursula R. Kattner, Albert V. Davydov, Eric S. Toberer, Vladan Stevanović, Aron Walsh, Nam‐Gyu Park, Alán Aspuru‐Guzik, Daniel P. Tabor, Jenny Nelson, James E. Murphy, Anant Setlur, John M. Gregoire, Hong Li, Ruijuan Xiao, Alfred Ludwig, Lane W. Martin, Andrew M. Rappe, Su‐Huai Wei, John D. Perkins

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The 2019 materials by design roadmapOriginalIntegrated computational materials engineering for advanced materials: A brief review182The promise of artificial intelligence in chemical engineering: Is it here, finally?665A methodological review on material growth and synthesis of solar-driven water splitting photoelectrochemical cells40ABO 3 and A 1−x C x B 1−y D y (O 1−z E z ) 3 : review of experimental optimisation of thin film perovskites by high-throughput evaporative physical vapour deposition8Inertial effective mass as an effective descriptor for thermoelectrics via data-driven evaluation70Making Machine Learning Work in Chemistry: Methodological Innovation, Software Development, and Application Studies3Nanoinformatics, and the big challenges for the science of small things92Progress and prospects for accelerating materials science with automated and autonomous workflows228Symmetry based molecular design for triplet excitation and optical spin injection6Interplay between Composition, Electronic Structure, Disorder, and Doping due to Dual Sublattice Mixing in Nonequilibrium Synthesis of ZnSnN2:O48Accessing Metastability in Heterostructural Semiconductor Alloys9Advances of machine learning in molecular modeling and simulation128Analyzing machine learning models to accelerate generation of fundamental materials insights91Factors Controlling Open-Circuit Voltage Losses in Organic Solar Cells170Finding the needle in the haystack: Materials discovery and design through computational ab initio high-throughput screening49Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures48Multiscale modelling of photoinduced processes in composite systems114Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia77Toward Design of Novel Materials for Organic Electronics167Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries199Network analysis of synthesizable materials discovery115Completing the picture through correlative characterization128A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning120Thin-Film Processing Routes for Combinatorial Materials Investigations—A Review80Symbolic regression in materials science302Status of materials and device modelling for kesterite solar cells41Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods357Multi-component background learning automates signal detection for spectroscopic data32Exchange bias and large room temperature magnetoresistance in ion beam-synthesized Co nanoparticles in SiO29Tracking materials science data lineage to manage millions of materials experiments and analyses62Computationally Driven Discovery of a Family of Layered LiNiB Polymorphs39Computationally Driven Discovery of a Family of Layered LiNiB Polymorphs9The GIPAW approach for the study of local structures and the electric field gradients at Cd and Ta impurity sites. Application to doped yttria ceramics4An informatics software stack for point defect-derived opto-electronic properties: the Asphalt Project19Data‐Driven Materials Science: Status, Challenges, and Perspectives700Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals84Discovery of blue singlet exciton fission molecules via a high-throughput virtual screening and experimental approach42Autonome Entdeckung in den chemischen Wissenschaften, Teil II: Ausblick4Autonomous Discovery in the Chemical Sciences Part II: Outlook245Materials property prediction using symmetry-labeled graphs as atomic position independent descriptors17Quantum Materials with Atomic Precision: Artificial Atoms in Solids: Ab Initio Design, Control, and Integration of Single Photon Emitters in Artificial Quantum Materials16From NaZn4Sb3 to HT-Na1–xZn4–ySb3: Panoramic Hydride Synthesis, Structural Diversity, and Thermoelectric Properties28Review of high-throughput approaches to search for piezoelectric nitrides17Learning acoustic emission signatures from a nanoindentation-based lithography process: Towards rapid microstructure characterization18The Materials Research Platform: Defining the Requirements from User Stories31High-throughput fabrication and semi-automated characterization of oxide thin film transistors1Towards rapid, in situ characterization for materials-on-demand manufacturing6Coordinated Optical Matching of a Texture Interface Made from Demixing Blended Polymers for High-Performance Inverted Perovskite Solar Cells93Practical challenges in the development of photoelectrochemical solar fuels production102New Family of Anisotropic Zinc-Based Semiconductors in a Shallow Energy Landscape9A combinatorial guide to phase formation and surface passivation of tungsten titanium oxide prepared by thermal oxidation14Examination of high-throughput hybrid calculations using coarser reciprocal space meshes10Benchmarking the acceleration of materials discovery by sequential learning151Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design19High-throughput computational screening for solid-state Li-ion conductors140Machine-enabled inverse design of inorganic solid materials: promises and challenges190Machine-learning-assisted search for functional materials over extended chemical space25New Materials in Architecture: A Pedagogical Approach to Materials by Design1Recent advances in bioelectronics chemistry111The uncertainty pyramid for electronic-structure methods9Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices64Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods52Machine learning and big scientific data129Origin of High Thermoelectric Performance in Earth-Abundant Phosphide–Tetrahedrite43Towards high-performance electrochemical thermal energy harvester based on ferrofluids21Effect of high oxygen deficiency in nano-confined bismuth sesquioxide2A Design-to-Device Pipeline for Data-Driven Materials Discovery114A microarray platform designed for high-throughput screening the reaction conditions for the synthesis of micro/nanosized biomedical materials15Computational functionality‐driven design of semiconductors for optoelectronic applications58Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation78Theoretical search for possible Li–Ni–B crystal structures using an adaptive genetic algorithm16Methods for comparing uncertainty quantifications for material property predictions205Predicting structure zone diagrams for thin film synthesis by generative machine learning60Experimental, computational and thermodynamic studies in perovskites metal oxides for thermochemical fuel production: A review94Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery99Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique89Combinatorial Synthesis of Oxysulfides in the Lanthanum–Bismuth-Copper System4Utilizing Site Disorder in the Development of New Energy-Relevant Semiconductors73Accuracy of Hybrid Functionals with Non-Self-Consistent Kohn–Sham Orbitals for Predicting the Properties of Semiconductors36On the Dopability of Semiconductors and Governing Material Properties49Opportunities and Challenges for Machine Learning in Materials Science522Metastability at Defective Metal Oxide Interfaces and Nanoconfined Structures45Automated crystal structure analysis based on blackbox optimisation58Exploring chemical compound space with quantum-based machine learning367Machine-learning-assisted fabrication: Bayesian optimization of laser-induced graphene patterning using in-situ Raman analysis86Artificial Intelligence Applied to the Prediction of Organic Materials12High-temperature conventional superconductivity in the boron-carbon system: Material trends33A database of battery materials auto-generated using ChemDataExtractor162Simplifying inverse material design problems for fixed lattices with alchemical chirality0Searching for New Ferroelectric Materials Using High-Throughput Databases: An Experimental Perspective on BiAlO 3 and BiInO 327Trigonal polymorph of Li2MnO34Exploring the A2BX3 Family for New Functional Materials Using Crystallographic Database Mining and First-Principles Calculations5Modelling and understanding battery materials with machine-learning-driven atomistic simulations113Augmenting machine learning of energy landscapes with local structural information16Artificial intelligence for photonics and photonic materials63Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends146Beyond Expectation: Advanced Materials Design, Synthesis, and Processing to Enable Novel Ferroelectric Properties and Applications1Progress in Modeling of Biomass Fast Pyrolysis: A Review77Coupling physics in machine learning to predict properties of high-temperatures alloys95High-throughput design of magnetic materials52Networks of materials: Construction and structural analysis8Ecospheric Decontamination Attained via Green Nanobiotechnological NiO-Based Nanocatalyst Derived from Nature’s Biofactories9POMzites: A roadmap for inverse design in metal oxide chemistry6Towards Experimental Handbooks in Catalysis63Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning128Electrochemistry: Retrospect and Prospects7Leverage electron properties to predict phonon properties via transfer learning for semiconductors45Thermoelectric Oxide Ceramics Outperforming Single Crystals Enabled by Dopant Segregations34Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys13Generalized small set of ordered structures method for the solid-solution phase of high-entropy alloys18Machine Learned Model for Solid Form Volume Estimation Based on Packing-Accessible Surface and Molecular Topological Fragments17Die Materialsynthesemaschine1Perovskite-inspired materials for photovoltaics and beyond—from design to devices206Self-Driving Laboratories Coming of Age15Artificial neural networks for predicting charge transfer coupling50High-throughput calculations based on the small set of ordered structures method for non-equimolar high entropy alloys15Can we predict materials that can be synthesised?73Solution Combustion Synthesis of Transparent Conducting Thin Films for Sustainable Photovoltaic Applications17CRYSPNet: Crystal structure predictions via neural networks42Application of Multiscale Mechanics Methods in Structural Integrity Analysis4Bright aspects of defects and dark traits of dopants in the photoluminescence of Er 2 X 2 O 7 :Eu 3+ (X = Ti and Zr) pyrochlore: an insight using EXAFS, positron annihilation and DFT11Engineering structure and functionalities of chemical vapor deposited photocatalytic titanium dioxide films through different types of precursors6Generative models for inverse design of inorganic solid materials38High-throughput design of magnetic materials3Machine Learning Applications in Anti-cancer Drug Discovery1Molecular Simulation and Statistical Learning Methods toward Predicting Drug–Polymer Amorphous Solid Dispersion Miscibility, Stability, and Formulation Design80Impact of noble-gas filler atoms on the lattice thermal conductivity of CoSb 3 skutterudites: first-principles modelling15How the Shape of Chemical Data Can Enable Data-Driven Materials Discovery18Opportunities and challenges of text mining in materials research180Polymorphism in Post-Dichalcogenide Two-Dimensional Materials126Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration65Thermal Transport in Polymers: A Review120The Value of Watching How Materials Grow: A Multimodal Case Study on Halide Perovskites20Rapid Recombination by Cadmium Vacancies in CdTe89A hybrid virtual–boundary element formulation for heterogeneous materials17Combinatorial Materials Design Approach to Investigate Adhesion Layer Chemistry for Optimal Interfacial Adhesion Strength6Chalcogenide perovskites for photovoltaics: current status and prospects94Theoretical Prediction of Two-Dimensional Materials, Behavior, and Properties66Materials graph ontology23What's Left for a Computational Chemist To Do in the Age of Machine Learning?10Transparent Conducting Electrodes for Optoelectronic Devices: State-of-the-art and Perspectives5A NEA review on innovative structural materials solutions, including advanced manufacturing processes for nuclear applications based on technology readiness assessment20Simplifying inverse materials design problems for fixed lattices with alchemical chirality19ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold1Materials Representation and Transfer Learning for Multi-Property Prediction4Materials representation and transfer learning for multi-property prediction71Benchmarking graph neural networks for materials chemistry332Concurrent goal-oriented materials-by-design0Characterization of microscopic deformation of materials using deep learning algorithms9Predictive Synthesis53

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  50. New Family of Anisotropic Zinc-Based Semiconductors in a Shallow Energy Landscape

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