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RESEARCH PAPER · Proceedings of the National Academy of Sciences · 2018

Learning atoms for materials discovery

Quan Zhou, Peizhe Tang, Shenxiu Liu, Jinbo Pan, Qimin Yan, Shou-Cheng Zhang

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Learning atoms for materials discoveryOriginalFeature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements140Learning from scratch2Uses of Artificial Intelligence in Health4Chemical Reaction Spectrum0Chemical Reaction Spectrum0Compressing physical properties of atomic species for improving predictive chemistry29Hierarchical visualization of materials space with graph convolutional neural networks69A System for Learning Atoms Based on Long Short-Term Memory Recurrent Neural Networks28Alloy design in the 21st century: ICME, materials genome, and artificial intelligence strategies3Band gap prediction for large organic crystal structures with machine learning70iFLEX: A Fully Open-Source, High-Density Field-Programmable Gate Array (FPGA)-Based Hardware Co-Processor for Vector Similarity Searching2Nanoinformatics, and the big challenges for the science of small things92References0Creating Machine Learning-Driven Material Recipes Based on Crystal Structure46Multiscale simulation on thermal stability of supported metal nanocatalysts11Classifying superheavy elements by machine learning12Analyzing machine learning models to accelerate generation of fundamental materials insights91Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors52Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals1525Machine Learning to Instruct Single Crystal Growth by Flux Method*16Transfer Learning for Scientific Data Chain Extraction in Small Chemical Corpus with BERT-CRF Model5Strategies for Pre-training Graph Neural Networks158Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data129Mapping of atomic catalyst on graphdiyne83Simulation and design of energy materials accelerated by machine learning74Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials136Band Gap Prediction for Large Organic Crystal Structures with Machine Learning77Unsupervised word embeddings capture latent knowledge from materials science literature1115IRNet47Atom table convolutional neural networks for an accurate prediction of compounds properties98Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism1053Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validation337Emerging role of machine learning in light-matter interaction83A novel molecular representation with BiGRU neural networks for learning atom165Controversies, compromises and the common chemical language2Autonome Entdeckung in den chemischen Wissenschaften, Teil II: Ausblick4Autonomous Discovery in the Chemical Sciences Part II: Outlook245Inverse Design of Solid-State Materials via a Continuous Representation379Deep neural network method for predicting the mechanical properties of composites156Learning acoustic emission signatures from a nanoindentation-based lithography process: Towards rapid microstructure characterization18Self-Learning Perfect Optical Chirality via a Deep Neural Network101Unsupervised discovery of solid-state lithium ion conductors312Preserving Composition and Crystal Structures of Chemical Compounds in Atomic Embedding1Multi-channel convolutional neural networks for materials properties prediction31Can machine learning find extraordinary materials?119Autonomous intelligent agents for accelerated materials discovery95Directly predicting limiting potentials from easily obtainable physical properties of graphene-supported single-atom electrocatalysts by machine learning173Machine learning and computation: exploring structure-property correlations in inorganic crystalline materials2Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures221Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures205Prediction of Energy Gaps in Graphene—Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks2The Next Breakthroughs of Artificial Intelligence: The Interdisciplinary Nature of AI42Harnessing Legacy Data to Educate Data-Enabled Structural Materials Engineers4Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning34Artificial Intelligence to Power the Future of Materials Science and Engineering154Application of Symmetry Functions to Large Chemical Spaces Using a Convolutional Neural Network5Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions70Designing bioinspired brick-and-mortar composites using machine learning and statistical learning43Drug vector representation: a tool for drug similarity analysis5Exploring the landscape between synthetic and biosynthetic materials discovery: important considerations via systems connectivity, cooperation and scale-driven convergence in biomanufacturing9Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy115Accelerated design and characterization of non-uniform cellular materials via a machine-learning based framework90Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique89SemanticGO: a tool for gene functional similarity analysis in Arabidopsis thaliana and rice4Data-driven predictive models for chemical durability of oxide glass under different chemical conditions24Predicting new superconductors and their critical temperatures using machine learning73High-Throughput Screening of Hydrogen Evolution Reaction Catalysts in MXene Materials112An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery94Is Domain Knowledge Necessary for Machine Learning 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infinite-layer versus perovskite oxides13Atomic descriptors generated from coordination polyhedra in crystal structures6A critical review: Recent advances in “digital” biomolecule detection with single copy sensitivity68High-Entropy Ceramics4Descriptors of atoms and structure information for predicting properties of crystalline materials6Predicting Material Properties Using a 3D Graph Neural Network with Invariant Local Descriptors1Enabling deeper learning on big data for materials informatics applications67Recreation of the periodic table with an unsupervised machine learning algorithm10Graph-based deep learning frameworks for molecules and solid-state materials37Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning22Progress on material characterization methods under big data environment28Edge-state transport in circular quantum point contact quantum piezotronic transistors4A deep neural network inverse solution to recover pre-crash impact data of car collisions47Application of artificial intelligence for predicting reaction results in advanced oxidation processes27Structure motif-centric learning framework for inorganic crystalline systems.24Structure motif–centric learning framework for inorganic crystalline systems34Building Machine Learning systems for multi-atoms structures: CH3NH3PbI3 perovskite nanoparticles36Materials Fingerprinting Classification13Deep learning method for predicting the mechanical properties of aluminum alloys with small data sets52High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks138Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening52Deep Learning-Based Prediction of Material Properties Using Chemical Compositions and Diffraction Patterns as Experimentally Accessible Inputs10Leveraging Transfer Learning and Chemical Principles toward Interpretable Materials Properties9To See a World in a Grain of Sand0Atomtransmachine: An atomic feature representation model for machine learning4Machine learning-based discovery of molecules, crystals, and composites: A perspective review16GeoAI and Deep Learning9Search methods for inorganic materials crystal structure prediction27Optical Fingerprint Classification of Single Upconversion Nanoparticles by Deep Learning23Symbolic Transformer Accelerating Machine Learning Screening of Hydrogen and Deuterium Evolution Reaction Catalysts in MA2Z4 Materials60Structures and electronic properties of functional molecules on metal substrates: From single molecule to self‐assemblies9Applications of machine learning in computational nanotechnology13Machine learning assisted hierarchical filtering: a strategy for designing magnets with large moment and anisotropy energy14Predicting the Materials Properties Using a 3D Graph Neural Network With Invariant Representation15Recent trends in computational tools and data-driven modeling for advanced materials85From Platform to Knowledge Graph: Evolution of Laboratory Automation108A review of the recent progress in battery informatics178Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties5Quantifying transfer learning synergies in infinite-layer and perovskite nitrides, oxides, and fluorides6Distributed representations of atoms and materials for machine learning41Integrating human cognition in cyber-physical systems: A multidimensional fuzzy pattern model with application to thermal spraying37Multi-dimensional characteristic construction methods of computational materials under big data environment1Text Mining for Energy Materials3Recent advances and applications of deep learning methods in materials science1161Machine‐Learning and Data‐Intensive Methods for Accelerating the Development of Rechargeable Battery Chemistries: A Review6Prediction of robust scientific facts from literature29Autonomous materials discovery and manufacturing (AMDM): A review and perspectives18Predicting Material Properties by Deep Graph Networks7S2SNet: A Pretrained Neural Network for Superconductivity Discovery5Misalignment Between Skills Discovered, Disseminated, and Deployed in the Knowledge Economy2Machine learning for high-entropy alloys: Progress, challenges and opportunities397Prediction of formation energies of UCr4C4-type compounds from Magpie feature descriptor-based machine learning approaches9Assessing Deep Generative Models in Chemical Composition Space34Prediction of band gap of transition metal sulfide with Janus structure by deep learning atomic feature representation method3Self-supervised learning of materials concepts from crystal structures via deep neural networks25Upcoming Transformations in Integrated Energy/Chemicals Sectors: Some Challenges and Several Opportunities27Applications of convolutional neural networks for spectral analysis1Machine learning descriptors in materials chemistry: prediction and experimental validation synthesis of novel intermetallic UCd36Learning from mistakes: Sampling strategies to efficiently train machine learning models for material property prediction21Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering65Machine-Learning Approaches for the Discovery of Electrolyte Materials for Solid-State Lithium Batteries29Representations of Materials for Machine Learning133A proposed test for human-level intelligence in AI1Data Mining in Material Science1Improving deep learning model performance under parametric constraints for materials informatics applications12Pre-Activation based Representation Learning to Enhance Predictive Analytics on Small Materials Data7Machine Learning-Driven Discovery of Key Descriptors for CO 2 Activation over Two-Dimensional Transition Metal Carbides and Nitrides54Outliers in Shannon’s effective ionic radii table and the table extension by machine learning34Information Lattice Learning11Deep learning accelerates the development of Ni-based single crystal superalloys: A physical-constrained neural network for creep rupture life prediction37Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions38

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