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RESEARCH PAPER · Computational Materials Science · 2016

Multi-fidelity machine learning models for accurate bandgap predictions of solids

Ghanshyam Pilania, James E. Gubernatis, Turab Lookman

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Multi-fidelity machine learning models for accurate bandgap predictions of solidsOriginalPredicting electronic structure properties of transition metal complexes with neural networks196Predicting electronic structure properties of transition metal complexes with neural networks122An Empirical, yet Practical Way To Predict the Band Gap in Solids by Using Density Functional Band Structure Calculations296Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships287Machine learning in materials informatics: recent applications and prospects1912Quantum Machine Learning in Chemistry and Materials19Machine learning properties of binary wurtzite superlattices31Predicting the Band Gaps of Inorganic Solids by Machine Learning456Alchemical and structural distribution based representation for universal quantum machine learning334Physics-informed machine learning for inorganic scintillator discovery45A strategy to apply machine learning to small datasets in materials science776AFLOW-ML: A RESTful API for machine-learning predictions of materials properties112Fast predictive models based on multi-fidelity sampling of properties in molecular dynamics simulations21Discovering a Transferable Charge Assignment Model Using Machine Learning141Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations123Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks121Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry159NOMAD 2018 Kaggle Competition: Solving Materials Science Challenges Through Crowd Sourcing19Active learning for accelerated design of layered materials170Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds117Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited126Machine learning in materials design and discovery: Examples from the present and suggestions for the future198Machine learning for renewable energy materials312Prediction of Mohs Hardness with Machine Learning Methods Using Compositional Features4Toward a Predictive Hierarchical Multiscale Modeling Approach for Energetic Materials17Neural Network Analysis of Dynamic Fracture in a Layered Material3Electrical Doping Effect of Vacancies on Monolayer MoS263Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation47Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra262Materials informatics: From the atomic-level to the continuum189Learning-in-Templates Enables Accelerated Discovery and Synthesis of New Stable Double Perovskites37Feature Engineering for Materials Chemistry—Does Size Matter?10Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO185Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design828Electronic transport of organic-inorganic hybrid perovskites from first-principles and machine learning23Efficient construction method for phase diagrams using uncertainty sampling61Machine Learning Augmented Discovery of Chalcogenide Double Perovskites for Photovoltaics86Discovering unusual structures from exception using big data and machine learning techniques35Materials Discovery of Stable and Nontoxic Halide Perovskite Materials for High‐Efficiency Solar Cells90Systems Approaches to Materials Design: Past, Present, and Future79Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia77General Multi-Fidelity Framework for Training Artificial Neural Networks With Computational Models46Experiment Design Frameworks for Accelerated Discovery of Targeted Materials Across Scales30Data analysis of multi-dimensional thermophysical properties of liquid substances based on clustering approach of machine learning13Gap Prediction in Hybrid Graphene-Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks8Bayesian optimization of chemical composition: A comprehensive framework and its application to RFe12 -type magnet compounds46Chemical diversity in molecular orbital energy predictions with kernel ridge regression78Chemical diversity in molecular orbital energy predictions with kernel ridge regression70A new MaterialGo database and its comparison with other high-throughput electronic structure databases for their predicted energy band gaps31General Atomic Neighborhood Fingerprint for Machine Learning-Based Methods50Application of materials informatics on crystalline materials for two-body terms approximation3Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery1023Alternative materials for perovskite solar cells from materials informatics29Machine learning models for the lattice thermal conductivity prediction of inorganic materials165Making machine learning a useful tool in the accelerated discovery of transition metal complexes47Recent advances and applications of machine learning in solid-state materials science2604Predicting Adsorption Energies Using Multifidelity Data23Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations52Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction55A multi-fidelity information-fusion approach to machine learn and predict polymer bandgap103Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials343Predicting charge density distribution of materials using a local-environment-based graph convolutional network65Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery82Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition63Machine Learning Stability and Bandgaps of Lead‐Free Perovskites for Photovoltaics64Predicting HSE band gaps from PBE charge densities via neural network functionals29Bayesian Global Optimization applied to the design of shape-memory alloys6Learning with Small Databases0Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity123Machine Learning Augmented Discovery of Chalcogenide Double Perovskites for Photovoltaics25Materials Representations1Multi-Fidelity Learning0Quantum Machine Learning in Chemistry and Materials18Searching for stable perovskite solar cell materials using materials genome techniques and high-throughput calculations40The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research78A Critical Review of Machine Learning of Energy Materials605Materials informatics approach to understand aluminum alloys24Fundamental band gap and alignment of two-dimensional semiconductors explored by machine learning*33Analysis of an Ultra-Short True Time Delay Line Optical Reservoir Computer7Machine learning: Accelerating materials development for energy storage and conversion370High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning56Quantum Chemistry in the Age of Machine Learning417Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods10Data integration for accelerated materials design via preference learning8Enhanced variable-fidelity surrogate-based optimization framework by Gaussian process regression and fuzzy clustering37Machine learning for multi-fidelity scale bridging and dynamical simulations of materials20Opportunities and Challenges for Machine Learning in Materials Science522Crystal Site Feature Embedding Enables Exploration of Large Chemical Spaces44Machine Learning in Chemistry77Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials88Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data30Big-Data Science in Porous Materials: Materials Genomics and Machine Learning597Exploring chemical compound space with quantum-based machine learning367Machine learning and excited-state molecular dynamics62Predicting Crystallization Tendency of Polymers Using Multifidelity Information Fusion and Machine Learning69Tuning the Conductivity Type in Monolayer WS2 and MoS2 by Sulfur Vacancies49Thousands of reactants and transition states for competing E2 and S N 2 reactions50An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery94Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning40Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling4Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys93Machine Learning Discovery of Computational Model Efficacy Boundaries8Predicting band gaps and band-edge positions of oxide perovskites using DFT and machine learning2Machine learning for halide perovskite materials130Thermochemical Data Fusion Using Graph Representation Learning9Voxelized Atomic Structure Potentials: Predicting Atomic Forces with the Accuracy of Quantum Mechanics Using Convolutional Neural Networks17Machine learning in materials design: Algorithm and application*62Leverage electron properties to predict phonon properties via transfer learning for semiconductors45Machine-learning predictions of polymer properties with Polymer Genome276Emerging materials intelligence ecosystems propelled by machine learning440Machine Learning for Electronically Excited States of Molecules397Machine learning for analyzing and characterizing InAsSb-based nBn photodetectors7Extraction of material properties through multi-fidelity deep learning from molecular dynamics simulation46Tuplewise Material Representation Based Machine Learning for Accurate Band Gap Prediction47Screening of perovskite materials for solar cell applications by first-principles calculations45Ab initio machine learning in chemical compound space4Polymer informatics: Current status and critical next steps272A data-driven approach to predicting band gap, excitation, and emission energies for Eu 2+ -activated phosphors28A machine learning approach using frequency descriptor for molecular property predictions3Computational techniques for characterisation of electrically conductive MOFs: quantum calculations and machine learning approaches50Experimental design for the highly accurate prediction of material properties using descriptors obtained by measurement9Machine Learning Accelerated Insights of Perovskite Materials3Machine-learning-assisted low dielectric constant polymer discovery49MUMBO: MUlti-task Max-Value Bayesian Optimization21Physically inspired deep learning of molecular excitations and photoemission spectra65Structural and elastic properties of binary semiconductors from energy gaps4Machine Learning Aided Design of Polymer with Targeted Band Gap Based on DFT Computation57Discovery of Lead-Free Hybrid Organic/Inorganic Perovskites Using Metaheuristic-Driven DFT Calculations30Learning properties of ordered and disordered materials from multi-fidelity data216Machine learning for advanced energy materials208Density functional theory-free descriptor for the practical discovery of perovskite catalysts10Exploring Heat-Shielding Nanoparticle-Based Materials via First-Principles Calculations and Transfer Learning4A bin and hash method for analyzing reference data and descriptors in machine learning potentials5Machine learning for materials design and discovery128A new model updating strategy with physics-based and data-driven models22Graph-based deep learning frameworks for molecules and solid-state materials37Design and engineering of high‐performance triboelectric nanogenerator for ubiquitous unattended devices68Machine learning in materials science: From explainable predictions to autonomous design280Material analysis and big data monitoring of sports training equipment based on machine learning algorithm21Polymer informatics with multi-task learning163Machine learning classification of binary semiconductor heterostructures17Correlation analysis of materials properties by machine learning: illustrated with stacking fault energy from first-principles calculations in dilute fcc-based alloys33Molecular excited states through a machine learning lens200Transfer learning based variable-fidelity surrogate model for shell buckling prediction93Perspective on integrating machine learning into computational chemistry and materials science203Computational discovery of energy materials in the era of big data and machine learning: A critical review60Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack14Catalyze Materials Science with Machine Learning66Autonomous experimentation systems for materials development: A community perspective398Bayesian Optimization in Materials Science: A Survey1

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