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RESEARCH PAPER · Chemical Science · 2018

Machine learning material properties from the periodic table using convolutional neural networks

Xiaolong Zheng, Peng Zheng, Ruizhi Zhang

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Machine learning material properties from the periodic table using convolutional neural networksOriginalDeep Learning: New Engine for the Study of Material Microstructures and Physical Properties3Formal structure of periodic system of elements21Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models86Formal structure of periodic system of elements29Application of deep learning neural network to identify collision load conditions based on permanent plastic deformation of shell structures73Recent advances and applications of machine learning in solid-state materials science2607Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validation337Challenges for the Periodic Systems of Elements: Chemical, Historical and Mathematical Perspectives18Multi-channel convolutional neural networks for materials properties prediction31Advances in optical and electrochemical techniques for biomedical imaging10In silico investigation of Cu(In,Ga)Se 2 -based solar cells3A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns199Searching high spin polarization ferromagnet in Heusler alloy via machine learning19A Critical Review of Machine Learning of Energy Materials606Machine learning for interatomic potential models363Application of Symmetry Functions to Large Chemical Spaces Using a Convolutional Neural Network5Gallium–Boron–Phosphide ($\hbox {GaBP}_{2}$): a new III–V semiconductor for photovoltaics11Targeting Productive Composition Space through Machine-Learning-Directed Inorganic Synthesis36Machine-Learning Assisted Screening of Energetic Materials71Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Enabling Nonlinear Manifold Projection Reduced-Order Models by Extending Convolutional Neural Networks to Unstructured Data.5Experimental inspection of a computationally-designed NiCrMnSi Heusler alloy with high Curie temperature5Emerging materials intelligence ecosystems propelled by machine learning441Exploiting deep learning for predictable carbon dot design53A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations11Accelerated design of promising mixed lead-free double halide organic–inorganic perovskites for photovoltaics using machine learning44Generative models for inverse design of inorganic solid materials38A general and transferable deep learning framework for predicting phase formation in materials93Recent Advances in Screening Lithium Solid-State Electrolytes Through Machine Learning25Predicting Material Properties Using a 3D Graph Neural Network with Invariant Local Descriptors1Atom classification with Machine Learning and correlations among physical properties of ZnO nanoparticle15Rare-class learning over Mg-doped ZnO nanoparticles32A deep neural network inverse solution to recover pre-crash impact data of car collisions47Application of deep transfer learning to predicting crystal structures of inorganic substances33Building Machine Learning systems for multi-atoms structures: CH3NH3PbI3 perovskite nanoparticles36Transfer learning for enhancing the homogenization-theory-based prediction of elasto-plastic response of particle/fiber-reinforced composites1Machine Learning in Solid‐State Chemistry: Heusler Compounds0Encoding the atomic structure for machine learning in materials science85Machine Learning Classifiers for Surface Crack Detection in Fracture Experiments63Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth16Development of artificial intelligence based model for the prediction of Young’s modulus of polymer/carbon-nanotubes composites47Predicting the thermodynamic stability of perovskite oxides using multiple machine learning techniques31Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores26Crystal graph attention networks for the prediction of stable materials153Application of Machine Learning in Determining the Mechanical Properties of Materials22Predicting the Materials Properties Using a 3D Graph Neural Network With Invariant Representation15Prediction Model for Tensile Modulus of Carbon Nanotube–Polymer Composites4Transfer learning for enhancing the homogenization-theory-based prediction of elasto-plastic response of particle/short fiber-reinforced composites49Representations and strategies for transferable machine learning improve model performance in chemical discovery19Deep learning modeling strategy for material science: from natural materials to metamaterials29Deep dive into machine learning density functional theory for materials science and chemistry111MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art70Data‐Driven Materials Innovation and Applications187Origins of magnetic memory and strong exchange bias bordering magnetic compensation in mixed-lanthanide systems3Enhanced Cellular Materials through Multiscale, Variable-Section Inner Designs: Mechanical Attributes and Neural Network Modeling20Reflections on one million compounds in the open quantum materials database (OQMD)49Rapidly predicting Kohn–Sham total energy using data-centric AI28Discovering constitutive equations of crystal structures by sparse identification3Neural network potentials for chemistry: concepts, applications and prospects128Intercalation Hosts for Multivalent‐Ion Batteries25A first-principles exploration of the conformational space of sodiated pyranose assisted by neural network potentials13Machine Learning Approaches for Predicting Crystal Systems: A Brief Review and a Case Study0What key contextual factors contribute to students’ reading literacy among top-performing countries and economies? Statistical and machine learning analyses16Transfer Learning in Inorganic Compounds’ Crystal Structure Classification1Perspectives on development of biomedical polymer materials in artificial intelligence age16Exploring a general convolutional neural network-based prediction model for critical casting diameter of metallic glasses21Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials109Accelerated prediction of perovskite material properties with classical machine learning and graph neural network6Representations of Materials for Machine Learning133The six stages of the convergence of the periodic system to its final structure3Geometries and electronic structures of Zr n Cu (n = 2–12) clusters: A joint machine-learning potential density functional theory investigation2Machine learning the spectral function of a hole in a quantum antiferromagnet6A materials informatics approach for composition and property prediction of polymer-derived silicon oxycarbides3Physically explainable statistical learning of flat bands in stoichiometric materials from the periodic table3Accelerating the Search for Stable Full Heusler Compounds through Machine Learning1Hardness prediction of high entropy alloys with periodic table representation of composition, processing, structure and physical parameters15Machine-learned constitutive relations for mechanoluminescent ZnS:Cu–PDMS composites2Probabilistic physics-guided transfer learning for material property prediction in extrusion deposition additive manufacturing12On Predefined-time Synchronization of Complex-valued Neural Networks With Inertial Terms1AI-driven inorganic materials by design0The Framework of Cyber Resilience Engineering Empowered by Endogenous Security and Safety2Machine learning assisted discovery of high-efficiency self-healing epoxy coating for corrosion protection41A data-driven method for optimization of classical interatomic potentials0A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks66Materials Properties Prediction (MAPP): Empowering the Prediction of Material Properties Solely Based on Chemical Formulas12Multi-scale computational study of high-temperature corrosion and the design of corrosion-resistant alloys33Time mesh independent framework for learning materials constitutive relationships1Exploring uncertainty in glass phase transitions through machine learning2Asparagus: A toolkit for autonomous, user-guided construction of machine-learned potential energy surfaces13Machine learning prediction of materials properties from chemical composition: Status and prospects29Clustering Cu-S based compounds using periodic table representation and compositional Wasserstein distance3The Periodic Table: Chemical Properties and Mendeleev Meets Physical Properties and Machine Learning1On the Sufficiency of a Single Hidden Layer in Feed-Forward Neural Networks Used for Machine Learning of Materials Properties5Artificial Intelligence and Li Ion Batteries: Basics and Breakthroughs in Electrolyte Materials Discovery20Atomic Scale Modeling via Molecular Dynamics and Machine Learning of Na2O-V2O5-TeO2 Glass-Based Cathodes0Enhancing material property prediction via a dual stream model fusing spatial and topological information2Self-reasoning assisted learning for non-Abelian gauge fields design2Application of Machine Learning in Nano Research — A Review2Leveraging generative models with periodicity-aware, invertible and invariant representations for crystalline materials design21Phase classification of high entropy alloys with composition, common physical, elemental-property descriptors and periodic table representation2Explainable Attention CNN for Predicting Properties of Heusler Alloys1Machine Learning-Driven Molecular Dynamics Investigation of the Physical and Heat Transfer Properties of Magnesium0Unsupervised learning techniques for clustering analysis of physicochemical properties in the periodic table elements4REBCO superconductors by pulsed laser deposition: Key innovations and large-scale applications11Interface Engineering and Optimization Strategies for High‐Energy‐Density Batteries Based on Polymer Composite Electrolytes36Machine learning prediction of HSE06-level band gaps in two-dimensional semiconductors with reference-guided graph neural networks0Emergent properties and the multiscale characterization challenge in condensed matter, from crystals to complex materials: a review9Physics-informed machine learning framework for predictive control of particle size distribution in Ni1/3Fe1/3Mn1/3(OH)2 synthesis2Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction0Predicting Materials Thermodynamics Enabled by Large Language Model‐Driven Dataset Building and Machine Learning1Motif-to-spectrum learning enables interpretable discovery and inverse design of optical absorbers0Data‐Driven Prediction of Block Copolymer Morphology Using Coarse‐Grained Modeling and Machine Learning1Per-MOF Mechanistic Attribution Reveals Context-Dependent Stability Mechanisms in Metal–Organic Frameworks0AI-Driven Design Platforms of Next-Generation Antibody Therapeutics0Generative Models in Inorganic Crystals Discovery and Inverse Design0

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