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RESEARCH PAPER · Nature · 2016

Can artificial intelligence create the next wonder material?

Nicola Nosengo

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Can artificial intelligence create the next wonder material?OriginalInversion of diffraction data for amorphous materials52Computers as Inventors – Legal and Policy Implications of Artificial Intelligence on Patent Law37Realistic inversion of diffraction data for an amorphous solid: The case of amorphous silicon23Synthesis of new materials by using fuzzy and big data concepts16Toward high value sensing: monolayer-protected metal nanoparticles in multivariable gas and vapor sensors91Design and Properties Prediction of AMCO3F by First-Principles Calculations9Machine Learning and Statistical Analysis for Materials Science: Stability and Transferability of Fingerprint Descriptors and Chemical Insights90Guidelines for rational design of high-performance absorbents: A case study of zeolite adsorbents for emerging pollutants in water19Quantifying similarity of pore-geometry in nanoporous materials186High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory255Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability202Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning202Decoding the glass genome112Polymer Informatics: Opportunities and Challenges324Towards a Hybrid Human-Computer Scientific Information Extraction Pipeline24Magnetism of new metastable cobalt-nitride compounds34Recent Advances of Modeling in Applied and Natural Sciences0Tuning mobility and stability of lithium ion conductors based on lattice dynamics248Theory of chemical bonds in metalloenzymes XXI. Possible mechanisms of water oxidation in oxygen evolving complex of photosystem II36Intelligent Monte Carlo: A New Paradigm for Inverse Polymerization Engineering37Recruiting machine learning methods for molecular simulations of proteins29Composition design for (PrNd–La–Ce) 2 Fe 14 B melt-spun magnets by machine learning technique5An open experimental database for exploring inorganic materials202Machine learning technique for prediction of magnetocaloric effect in La(Fe,Si/Al) 13 -based materials31Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing340New Type of Nitrides with High Electrical and Thermal Conductivities8Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape177Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering346Energy Materials Design for Steering Charge Kinetics14Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry270Vibrational Properties of Metastable Polymorph Structures by Machine Learning19The materials data ecosystem: Materials data science and its role in data-driven materials discovery6Integrated computational materials engineering for advanced materials: A brief review182Structures and evolution of metal oxide nanoclusters: Bottom-up genetic algorithm and fragment-based energy decomposition model2A predicting model for properties of steel using the industrial big data based on machine learning136Application of support vector machine on controlling the silanol groups of silica xerogel with the aid of segmented continuous flow reactor12From DFT to machine learning: recent approaches to materials science–a review849Intelligent Machine Learning: Tailor-Making Macromolecules33Structure prediction drives materials discovery717Carbon-containing multi-component thin films45First-Principles Design and Simulations Promote the Development of Nonlinear Optical Crystals299Synthesis of “iron-cast iron-glass” Obsolete Powder Composite Materials Using Fuzzy Logic1Structural, electronic and mechanical properties of all-sp2 carbon allotropes with density lower than graphene35Accelerating Materials Discovery Based on Generalized Low-dimensional Conformation Performance Relationships0Heat-resistant Pb( ii )-based X-ray scintillating metal–organic frameworks for sensitive dosage detection via an aggregation-induced luminescent chromophore42High throughput modelling of chiral carbon nanothread bundles0The partition principles for atomic-scale structures and their physical properties: application to the nonlinear optical crystal material KBe 2 BO 3 F 210Accelerated design of novel W-free high-strength Co-base superalloys with extremely wide γ/γʹ region by machine learning and CALPHAD methods98Single‐Atom Catalytic Materials for Advanced Battery Systems223Multi-component and high-entropy nitride coatings—A promising field in need of a novel approach162Machine learning in materials genome initiative: A review231DID Code: A Bridge Connecting the Materials Genome Engineering Database with Inheritable Integrated Intelligent Manufacturing11Modeling and dynamics of Wolbachia-infected male releases and mating competition on mosquito control41Accelerated design of L12-strengthened Co-base superalloys based on machine learning of experimental data44Small data materials design with machine learning: When the average model knows best35Integrating data mining and machine learning to discover high-strength ductile titanium alloys190The Effects of Hydrogen Distribution on the Elastic Properties and Hydrogen-Induced Hardening and Softening of α-Fe4Synthesis of Optimal Technological Parameters of “Iron-Cast-Glass” Grinding Composite Materials Using Fuzzy Logic and Big Data Concepts4Viscosity of Ionic Liquids1Reducing Crop Length of Rolling Material Using Predictive Model Analysis0Digital Transformation in Materials Science: A Paradigm Change in Material's Development72Text mining of hypereutectic Al-Si alloys literature based on active learning9Machine learning for advanced energy materials208Improving phase prediction accuracy for high entropy alloys with Machine learning109Artificial neural network modified constitutive descriptions for hot deformation and kinetic models for dynamic recrystallization of novel AZE311 and AZX311 alloys45Utilization of machine learning to accelerate colloidal synthesis and discovery32Value of medical imaging artificial intelligence in the diagnosis and treatment of new coronavirus pneumonia6Transition-metal-based chalcogenides: A rich source of infrared nonlinear optical materials184Inverse Design of Ferroelectric‐Order in Perovskite Crystal for Self‐Powered Ultraviolet Photodetection34Advancement of machine learning in materials science39Bridging the Gap Between Nanotoxicological Data and the Critical Structure–Activity Relationships4Machine learning-accelerated first-principles predictions of the stability and mechanical properties of L12-strengthened cobalt-based superalloys21Supplementary Data of ‘Discovering Novel Low Young's Modulus Nb–Ti–Zr Biomedical Alloys Via Combined Machine Learning and First Principles Approach’0Viscosity of Ionic Liquids3Fuel sorption into polymers: Experimental and machine learning studies13Machine-learning enabled thermodynamic model for the design of new rare-earth compounds35Deep learning modeling strategy for material science: from natural materials to metamaterials29An Oral English Evaluation Model Using Artificial Intelligence Method17Porous and permutable peptide frameworks18A First-Principles Study of F and Cl Doping in LiNi0.83Co0.08Mn0.08O2 Cathode Materials11Digital Twin Assistant Active Design and Optimization of Steel Mega-Sub Controlled Structural System under Severe Earthquake Waves2Emerging Deep-Sea Smart Composites: Advent, Performance, and Future Trends18Progress in nanomaterials fabrication and their prospects in artificial intelligence towards solid oxide fuel cells: A review34Advances in artificial intelligence applications in corroded reinforced concrete structures0Improving predictive modeling of polymeric materials using a hybrid approach of machine learning and expert intervention0Discovery of novel low modulus Nb–Ti–Zr biomedical alloys via combined machine learning and first principles approach23Constructing Microstructural Evolution System for Cement Hydration From Observed Data Using Deep Learning16Advances in machine learning- and artificial intelligence-assisted material design of steels109Artificial neural network–modified heat transfer model for furnace coil rolling of magnesium alloy plate2Full Metal Species Quantification of Supported Catalysts: Beyond Metal Dispersion3Intelligent Biomaterialomics: Molecular Design, Manufacturing, and Biomedical Applications51Predictive Synthesis of Copper Selenides Using a Multidimensional Phase Map Constructed with a Data-Driven Classifier32AI-enabled materials discovery for advanced ceramic electrochemical cells28Predicting atomic structure and mechanical properties in quinary L12-Strengthened cobalt-based superalloys using machine learning-driven first-principles calculations6The Use of Fuzzy IF-THEN Rules for Modeling Relationship Between Steel Alloy Composition and Its Characteristics0Machine learning assisted prediction in the discharge capacities of novel MXene cathodes for aluminum ion batteries16Accelerating Elastic Property Prediction in Fe-C Alloys through Coupling of Molecular Dynamics and Machine Learning9A machine-learning-based composition design of ternary Cu-based Rochow-Müller catalyst with high M2 selectivity5Materials descriptors of machine learning to boost development of lithium-ion batteries32Data-driven visualization of the dynamics of machine learning in materials research16Machine learning for CO 2 capture and conversion: A review60Thermodynamic Stability Prediction of Triple Transition‐Metal (Ti−Mo−V)3C2${\rm (Ti-Mo-V)}_3{\rm C}_2$ MXenes via Cluster Correlation‐Based Machine Learning2Triggerable Patches for Medical Applications27Deciphering the composition-microstructure correlation in low-density FeMnAlC steels with machine learning6Machine Learning‐Based Prediction of Supercapacitor Capacitance for MgCo2O4 Electrodes23Artificial intelligence enabled smart design and manufacturing of advanced materials: The endless Frontier in AI + era51Applications of artificial intelligence/machine learning to high-performance composites159Design and development of coating for metallic bipolar plates in proton exchange membrane fuel cell (PEMFC): A review56Explainable artificial intelligence for machine learning prediction of bandgap energies9Data-Quality-Navigated Machine Learning Strategy with Chemical Intuition to Improve Generalization13Predicting miscibility in binary compounds: a machine learning and genetic algorithm study1AI4Materials: Transforming the landscape of materials science and enigneering25Machine Learning-Based Methods for Materials Inverse Design: A Review34Computational understanding and multiscale simulation of secondary batteries14Quotient Complex (QC)-Based Machine Learning for 2D Hybrid Perovskite Design6Theoretical Insights into the Chemical Bonding, Electronic Structure, and Spectroscopic Properties of the Lanarkite Pb2SO5 Structure0Accelerated design and property validation of L12-strengthened Co-Ni-Cr-Al-Cu-Ti high-entropy superalloys based on unsupervised and supervised learning4Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency44On the Applicability of Generative Artificial Intelligence in Modelling the Dynamic Behaviour of Materials–Potentials and Pitfalls1Machine Learning in Next-Generation Polymer Composites: Recent Advances and Perspectives17The role and strategies of data-driven machine learning in design optimization and performance evaluation of electrochemical energy storage materials0Elucidating the Mechanism of Enhanced Strength and Degradability in Polylactic Acid via 2D Raman Imaging0Cross-scale regulation mechanisms of alloying elements on the mechanical properties of β-Ti alloys: a synergistic strategy to integrate machine learning and first-principles calculations0Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry105

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  5. Toward high value sensing: monolayer-protected metal nanoparticles in multivariable gas and vapor sensors

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  6. Design and Properties Prediction of AMCO3F by First-Principles Calculations

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