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RESEARCH PAPER · Computational Materials Science · 2019

Machine learning guided design of functional materials with targeted properties

Prasanna V. Balachandran

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Machine learning guided design of functional materials with targeted propertiesOriginalMaterials Design in Digital Era: Challenges and Opportunities4Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations8Future Frontiers in Corrosion Science and Engineering, Part III: The Next “Leap Ahead” in Corrosion Control May Be Enabled by Data Analytics and Artificial Intelligence20Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks41Defects and Aliovalent Doping Engineering in Electroceramics353Computational functionality‐driven design of semiconductors for optoelectronic applications58Adaptive characterization of microstructure dataset using a two stage machine learning approach64Characterizing fracture stress of defective graphene samples using shallow and deep artificial neural networks48Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals49An adaptive design approach for defects distribution modeling in materials from first-principle calculations20Method construction of structure-property relationships from data by machine learning assisted mining for materials design applications49A Bayesian machine learning approach for inverse prediction of high-performance concrete ingredients with targeted performance63Center-Environment Feature Model for Machine Learning Study of Spinel Oxides Based on First-Principles Computations40On the crystal chemistry of inorganic nitrides: crystal-chemical parameters, bonding behavior, and opportunities in the exploration of their compositional space17Progress in Modeling Compound Semiconductor Epitaxy: Unintentional Doping in GaN MOVPE17Machine learning aided design of perovskite oxide materials for photocatalytic water splitting156Cooperative Nature of Ferroelectricity in Two-Dimensional Hybrid Organic–Inorganic Perovskites39A new active learning approach for global optimization of atomic clusters24Exploring structure-composition relationships of cubic perovskite oxides via extreme feature engineering and automated machine learning10Studies on the regularity of perovskite formation via machine learning35Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search64Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers61Conditional physics informed neural networks60Innovative Materials Science via Machine Learning194Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage27Review of machine learning‐driven design of polymer‐based dielectrics50XERUS: An open-source tool for quick XRD phase identification and refinement automation15Recent progress in bismuth-based high Curie temperature piezo-/ferroelectric perovskites for electromechanical transduction applications88Theory-guided design of high-strength, high-melting point, ductile, low-density, single-phase BCC high entropy alloys67A multi-fidelity machine learning approach to high throughput materials screening77Discovery of Novel Photocatalysts Using Machine Learning Approach4Search without Coverage: Exploration Blind Spots in AI-Guided Materials Discovery0Analysis and evaluation of machine learning applications in materials design and discovery332D‐Nanofiller‐Based Polymer Nanocomposites for Capacitive Energy Storage Applications69Machine learning predictions of high-Curie-temperature materials19Machine Learning Enhanced High‐Throughput Fabrication and Optimization of Quasi‐2D Ruddlesden–Popper Perovskite Solar Cells43Classification of T6 Tempered 6XXX Series Aluminum Alloys Based on Machine Learning Principles9Machine learning in physics: A short guide12Accelerating the discovery of N-annulated perylene organic sensitizers via an interpretable machine learning model10On the use of Machine Learning modeling tools in chemical process and product engineering0Machine learning aided design of Bi2WO6/MIL-53(Al) nanocomposites11Design of a Novel Explainable Adversarial Autoencoder Model for the Electromagnetic Analysis of Functional Materials Based on Physics-Informed Learning4Machine learning approaches for screening of materials in flexible electronic devices0Nonlinear Potentiodynamic Battery Charging Protocols for Fun, Education, and Application4Physics-informed machine learning in asymptotic homogenization of elliptic equations8Accelerating the design and discovery of tribocorrosion-resistant metals by interfacing multiphysics modeling with machine learning and genetic algorithms16How to separate two Ts in a pod: Classifying T- and T′-type Ruddlesden-Popper cuprates by machine learning1Automated machine learning guides discovery of ABO3-type oxides for effective water splitting photocatalysis7ZnO-Based Photocatalysts: Synergistic Effects of Material Modifications and Machine Learning Optimization28Covalent organic framework-based solid polymer electrolytes for metal-ion batteries: pioneering the future of DFT, MD, and ML techniques4Tandem Takeoff: Powering Tomorrow with Industrial‐Grade Perovskite/Silicon Solar Cells12Quantum Machine Learning for Drug Discovery: Taxonomy, Research Challenges, and the Road Ahead1Machine learning enabled discovery of photocatalytic ABX 3 perovskite materials0Molecular Property Prediction Toward Materials Science Applications Using Quantum-Inspired Graph Neural Networks0Sequential design optimization of high-variability additive manufacturing processes using Gaussian process regression1Machine learning insights into microstructural origins of transport and mechanical properties in porous microstructures2Developing open data for anomaly detection on nanoindentation curves0Engineered interfaces in electronic materials for energy-efficient computing0Inverse Design of Nanoparticulate Materials0

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