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RESEARCH PAPER · Journal of the American Chemical Society · 2018

Crystal Structure Prediction via Deep Learning

Kevin Ryan, Jeff Lengyel, Michael Shatruk

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Crystal Structure Prediction via Deep LearningOriginalPerspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering346Machine-Learning Enabled New Insights into the Coil-to-Globule Transition of Thermosensitive Polymers Using a Coarse-Grained Model48$3D$ Crystal Image Analysis based on Fast Synchrosqueezed Transforms0Rapid Phase Screening via Hydride Route: A Discovery of K8–xZn18+3xSb1626Prediction of Carbon Dioxide Adsorption via Deep Learning128Prediction of Carbon Dioxide Adsorption via Deep Learning74Deep Learning: New Engine for the Study of Material Microstructures and Physical Properties3Designing promising molecules for organic solar cells via machine learning assisted virtual screening115Machine learning for renewable energy materials312Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment Amines112Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets222Rational Crystal Polymorph Design of Olanzapine25Templating Structural Progessions in Intermetallics: How Chemical Pressure Directs Helix Formation in the Nowotny Chimney Ladders11Understanding Quantitative Relationship between Methane Storage Capacities and Characteristic Properties of Metal–Organic Frameworks Based on Machine Learning75Data mining new energy materials from structure databases57Topology: ToposPro42“Teamwork Makes the Dream Work”: Tribal Competition Evolutionary Search as a Surrogate for Free-Energy-Based Structural Predictions3Automated Stoichiometry Analysis of Single-Molecule Fluorescence Imaging Traces via Deep Learning79Gap Prediction in Hybrid Graphene-Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks8Crystal Structure Optimization and Gibbs Free Energy Comparison of Five Sulfathiazole Polymorphs by the Embedded Fragment QM Method at the DFT Level17Mechanistically Driven Control over Cubane Oxo Cluster Catalysts29Deep materials informatics: Applications of deep learning in materials science294When Does a Supramolecular Synthon Fail? Comparison of Bridgehead-Functionalized Adamantanes: The Tri- and Tetra-amides and Amine Hydrochlorides8Computationally Driven Discovery of a Family of Layered LiNiB Polymorphs39Computationally Driven Discovery of a Family of Layered LiNiB Polymorphs9Energy–Geometry Dependency of Molecular Structures: A Multistep Machine Learning Approach4Recent advances and applications of machine learning in solid-state materials science2604Data-Driven Discovery of Photoactive Quaternary Oxides Using First-Principles Machine Learning49Low-Temperature Polymorphic Transformation of β-Lactam Antibiotics6A deep neural network combined with molecular fingerprints (DNN-MF) to develop predictive models for hydroxyl radical rate constants of water contaminants107Inverse Design of Solid-State Materials via a Continuous Representation379Insights into CO2/N2 Selectivity in Porous Carbons from Deep Learning57Review of high-throughput approaches to search for piezoelectric nitrides17Unraveling Correlations between Molecular Properties and Device Parameters of Organic Solar Cells Using Machine Learning107Prediction Model of Organic Molecular Absorption Energies based on Deep Learning trained by Chaos-enhanced Accelerated Evolutionary algorithm6Multi-channel convolutional neural networks for materials properties prediction31Deep-learning-based quality filtering of mechanically exfoliated 2D crystals78pyGACE: Combining the genetic algorithm and cluster expansion methods to predict the ground-state structure of systems containing point defects7Deep Learning Theoretical Chapter for Chemometrician0Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields19In silico investigation of Cu(In,Ga)Se 2 -based solar cells3Integrating Physics-Based Modeling with Machine Learning: A Survey285Machine-enabled inverse design of inorganic solid materials: promises and challenges190The CSD and knowledge databases: from answers to questions22A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns199A Universal Machine Learning Algorithm for Large-Scale Screening of Materials178Monitoring sugar crystallization with deep neural networks61A perspective on conventional high-temperature superconductors at high pressure: Methods and materials523Symmetry-Based Crystal Structure Enumeration in Two Dimensions9Inverse Design of Potential Singlet Fission Molecules using a Transfer Learning Based Approach1Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein Energy50Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals49A handle on the scandal: Data driven approaches to structure prediction4Theory-training deep neural networks for an alloy solidification benchmark problem39A perspective on conventional high-temperature superconductors at high pressure: Methods and materials434Two-Dimensional Nanomaterials with Unconventional Phases137Studied and Forgotten. 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for Predicting Protein Infrared Spectra107Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles58Ultra-fast and accurate binding energy prediction of shuttle effect-suppressive sulfur hosts for lithium-sulfur batteries using machine learning91Na1.7Li0.3BaZn7(TeO6)3: The first mixed alkali metal and alkaline earth metal zinc tellurate0The XtalOpt Evolutionary Algorithm for Crystal Structure Prediction89Artificial neural network potential for pure zinc33Distance Matrix-Based Crystal Structure Prediction Using Evolutionary Algorithms6Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach125Borates: A Rich Source for Optical Materials1089Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning51CRYSPNet: Crystal structure predictions via neural networks42Doping Lanthanide Ions in Colloidal Semiconductor Nanocrystals for Brighter Photoluminescence241A data-driven XRD analysis protocol for phase identification and phase-fraction prediction of multiphase inorganic compounds58Atomic descriptors generated from coordination polyhedra in crystal structures6Deep Learning Approach for Chemistry and Processing History Prediction From Materials Microstructure: Application to Spinodal Decomposition1Deep learning for material synthesis and manufacturing systems: A review74Determining usefulness of machine learning in materials discovery using simulated research landscapes15Real-time prediction of 1 H and 13 C chemical shifts with DFT accuracy using a 3D graph neural network136Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry70Doping-Induced Charge Localization Suppresses Electron–Hole Recombination in Copper Zinc Tin Sulfide: Quantum Dynamics Combined with Deep Neural Networks Analysis27Inverse design of nanoporous crystalline reticular materials with deep generative models399Spontaneous Deracemizations187AlphaCrystal: Contact map based crystal structure prediction using deep learning8Recent Advances in Screening Lithium Solid-State Electrolytes Through Machine Learning25Toward efficient generation, correction, and properties control of unique drug‐like structures11Atom classification with Machine Learning and correlations among physical properties of ZnO nanoparticle15Accelerated crystal structure prediction of multi-elements random alloy using expandable features21Rare-class learning over Mg-doped ZnO nanoparticles32Ab initio determination of crystal stability of di-p-tolyl disulfide5Beyond Tolerance Factor: Using Deep Learning for Prediction Formability of ABX3 Perovskite Structures8Machine learning builds full-QM precision protein force fields in seconds22Application of deep transfer learning to predicting crystal structures of inorganic substances33Direct Quantification of Mixed Organic Acids Based on Spectral Image with Deep Learning4Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks102Building Machine Learning systems for multi-atoms structures: CH3NH3PbI3 perovskite nanoparticles36Deep neural networks in chemical engineering classrooms to accurately model adsorption equilibrium data33Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures152Multiscale Catalyst Design for Steam Methane Reforming Assisted by Deep Learning10Predicting inorganic dimensionality in templated metal oxides8Sensitivity of Intra- and Intermolecular Interactions of Benzo[h]quinoline from Car–Parrinello Molecular Dynamics and Electronic Structure Inspection4A deep convolutional neural network for real-time full profile analysis of big powder diffraction data59Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints31Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds17Statistical Learning from Single-Molecule Experiments: Support Vector Machines and Expectation–Maximization Approaches to Understanding Protein Unfolding Data3Emerging artificial intelligence in piezoelectric and triboelectric nanogenerators154Real-time Prediction of 1H and 13C Chemical Shifts with DFT accuracy using a 3D Graph Neural Network2Crystal structure prediction using age-fitness multi-objective genetic algorithm and coordination number constraints2Pattern Formation in Phase-Separated Langmuir and Langmuir Monolayer Films12Computational Tools to Rationalize and Predict the Self-Assembly Behavior of Supramolecular Gels32Machine Learning Force Fields: Recent Advances and Remaining Challenges127Neural evolution structure generation: High entropy alloys14Deep Learning Accelerates the Discovery of Two‐Dimensional Catalysts for Hydrogen Evolution Reaction57Crystal structure prediction of materials with high symmetry using differential evolution18Deep Learning for Voltammetric Sensing in a Living Animal Brain101Deep Learning for Voltammetric Sensing in a Living Animal Brain17Machine-Learning-Enabled Tricks of the Trade for Rapid Host Material Discovery in Li–S Battery47Crystal-Site-Based Artificial Neural Networks for Material Classification5Virtual experimentations by deep learning on tangible materials25Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth15Development of artificial intelligence based model for the prediction of Young’s modulus of polymer/carbon-nanotubes composites47A graph theoretic representation and analysis of zeolite frameworks20Differentiable thermodynamic modeling17Machine learning for the design and discovery of zeolites and porous crystalline materials41Application of Deep Neural Network Models in Drug Discovery Programs45Predicting the thermodynamic stability of perovskite oxides using multiple machine learning techniques31Screening of potential double perovskite materials for photovoltaic applications using agglomerative hierarchical clustering1Predicting synthesizability of crystalline materials via deep learning43A conceptual study of transfer learning with linear models for data-driven property prediction19Transfer Learning-Based Algorithms for the Detection of Fatigue Crack Initiation Sites: A Comparative Study12A machine learning approach for predicting the nucleophilicity of organic molecules28Application of Machine Learning in Determining the Mechanical Properties of Materials22

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