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RESEARCH PAPER · Frontiers in Materials · 2016

Theory-Guided Machine Learning in Materials Science

Nicholas Wagner, James M. Rondinelli

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Theory-Guided Machine Learning in Materials ScienceOriginalElucidating multi-physics interactions in suspensions for the design of polymeric dispersants: a hierarchical machine learning approach36Developing Comprehensive Computational Parameter Sets To Describe the Performance of Pyridine-Oxazoline and Related Ligands89Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data1667Statistical Analysis of Coordination Environments in Oxides187Decoding the glass genome112Assessing Local Structure Motifs Using Order Parameters for Motif Recognition, Interstitial Identification, and Diffusion Path Characterization85Naïve Bayes classifier, multivariate linear regression and experimental testing for classification and characterization of wheat straw based on mechanical properties29Representing molecular and materials data for unsupervised machine learning27Constitutive models of magnetorheological fluids having temperature-dependent prediction parameter57Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr0.2Ti0.8O3 Thin Films26Learning from Correlations Based on Local Structure: Rare-Earth Nickelates Revisited21A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method43Effects of substitution element on physical properties of BiFeO3-BaTiO3 ferroelectric ceramics driven by data-mining oxide perovskites2Towards Simulation-Data Science – A Case Study on Material Failures1Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach143A cross-comparison of feature selection algorithms on multiple cyber security data-sets.11Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning33Materials informatics: From the atomic-level to the continuum189Visualising multi-dimensional structure/property relationships with machine learning31Finding the needle in the haystack: Materials discovery and design through computational ab initio high-throughput screening49Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data53Machine learning in agricultural and applied economics216Properties-to-microstructure-to-processing Inverse Analysis for Steels via Machine Learning20Data-driven crack assessment based on surface measurements4An Iterative Scientific Machine Learning Approach for Discovery of Theories Underlying Physical Phenomena4Digital Twin: Values, Challenges and Enablers73A theory-guided deep-learning formulation and optimization of seismic waveform inversion235Reliable and explainable machine-learning methods for accelerated material discovery211A Machine Learning Tool for Materials Informatics13Machine Learning Approaches for Thermoelectric Materials Research231Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation70Cuboctahedral stability in Titanium halide perovskites via machine learning44Quantitative structure-property relationship (QSPR) analysis of calcium aluminosilicate glasses based on molecular dynamics simulations49Digital Twin: Values, Challenges and Enablers From a Modeling Perspective1901Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus315Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity123Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials50Machine learning reveals multiple classes of diamond nanoparticles25Model-based research toward design of innovative materials: molecular weight prediction of bridged polysilsesquioxanes6Replicating Machine Learning Experiments in Materials Science12Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods52Property Predictions for Dual‐Phase Steels Using Persistent Homology and Machine Learning22A Critical Review of Machine Learning of Energy Materials605Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach41An Evolve-Then-Correct Reduced Order Model for Hidden Fluid Dynamics22Theory-guided machine learning for damage characterization of composites152Machine learning in materials genome initiative: A review231The promise of implementing machine learning in earthquake engineering: A state‐of‐the‐art review496Big-Data Science in Porous Materials: Materials Genomics and Machine Learning597Data assimilation empowered neural network parameterizations for subgrid processes in geophysical flows8Featureless approach for predicting Critical Temperature of Superconductors5Machine learning prediction of self-diffusion in Lennard-Jones fluids54Is Domain Knowledge Necessary for Machine Learning Materials Properties?106Physik‐informierte Künstliche Intelligenz zur Berechnung und Bemessung im Stahlbau15Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys100Numerical analysis of a deep learning formulation of multi-parameter elastic full waveform inversion21Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials55Machine learning in materials design: Algorithm and application*62Invited review: Machine learning for materials developments in metals additive manufacturing231Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering181DeepMoD: Deep learning for model discovery in noisy data128Cyberattack Detection for Electric Vehicles Using Physics-Guided Machine Learning63Machine learning in agricultural economics6Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review43Material Design-for-eXcellence Framework – Application to Composites2Machine learning for material characterization with an application for predicting mechanical properties139A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications448Representation learning and predictive classification: Application with an electric arc furnace16Theory-Guided Machine Learning for Process Simulation of Advanced Composites2Atomistic modeling of meso-timescale processes with SEAKMC: A perspective and recent developments8Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows46Learning to Route via Theory-Guided Residual Network3Proxima9Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture311Predicting nepheline precipitation in waste glasses using ternary submixture model and machine learning24Applying machine learning for predicting thermal conductivity coefficient of polymeric aerogels30Knowledge-based Residual Learning12A machine learning approach for maximizing direct current power of nonlinear energy harvesting systems subjected to periodic impulse excitation5Toward Predictive Chemical Deformulation Enabled by Deep Generative Neural Networks7A deep learning approach for efficient topology optimization based on the element removal strategy39Dynamic model development for vehicle air conditioners based on physics-guided deep learning19Deep learning for solution and inversion of structural mechanics and vibrations17Inverse Design of Nanoparticles Using Multi‐Target Machine Learning41Chemical hardness-driven interpretable machine learning approach for rapid search of photocatalysts119Modeling of Seismic Energy Dissipation of Rocking Foundations Using Nonparametric Machine Learning Algorithms13Data-Driven Cyber-Attack Detection for PV Farms via Time-Frequency Domain Features41COMPARISON OF LINEAR REGRESSION MODELS OF THERMOPHYSICAL PROPERTIES WITH MODELS BASED ON MACHINE LEARNING1Functional Nanomaterials Design in the Workflow of Building Machine-Learning Models1Random Forest Regressor based superconductivity materials investigation for critical temperature prediction14Machine learning-assisted DFT reveals key descriptors governing the vacancy formation energy in Pd-substituted multicomponent ceria9Discovery and prediction capabilities in metal-based nanomaterials: An overview of the application of machine learning techniques and some recent advances47Machine learning in energy storage materials106Theory-inspired machine learning—towards a synergy between knowledge and data27Finding physical insights in catalysis with machine learning26Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery551Cold Sprayed Metallic Coatings on Fibre-Reinforced Composites: A Machine Learning Approach for the Optimization of the Process1An Empirical Evaluation of Constrained Feature Selection3Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137Leveraging Theory for Enhanced Machine Learning24Predictive model for the spreadability of cosmetic formulations based on large amplitude oscillatory shear (LAOS) and machine learning20Magnetic order driven ultrafast phase transition in NdNiO324Numerical prediction of microstructure and hardness for low carbon steel wire Arc additive manufacturing components69Quantitative high-throughput measurement of bulk mechanical properties using commonly available equipment11Comparing numerical modelling, traditional machine learning and theory-guided machine learning in inverse modeling of groundwater dynamics: A first study case application22Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes166Emerging Trends in Machine Learning: A Polymer Perspective187Identification of damage properties of glass/epoxy laminates using machine learning models14Quantification of similarity and physical awareness of microstructures generated via generative models18Machine learning and molecular dynamics based models to predict the temperature dependent elastic properties of silver nanowires5Advances in machine learning- and artificial intelligence-assisted material design of steels109A convolutional neural network-based model that predicts acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation10Machine-learning-based prediction of cubic perovskite formation energy and magnetism3Integrating theory with machine learning for predicting polymer solution phase behavior31Silica Aerogel Synthesis/Process–Property Predictions by Machine Learning37Machine learning for predicting ZT values of high-performance thermoelectric materials in mid-temperature range17Design and Fabrication of Automatic Cattle Feeder System Using IOT0In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Forecasting cross-tie condition based on the dynamic adjacent support using a theory-guided neural network model2An Accelerated Process Optimization Method to Minimize Deformations in Composites Using Theory-guided Probabilistic Machine Learning31Using Machine Learning Techniques to Discover Novel Thermoelectric Materials4Scope of machine learning in materials research—A review229Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science4Ética e inteligência artificial0Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective76Viscosity of Ionic Liquids: Theories and Models82Application of artificial intelligence techniques for the verification of pile capacity at construction site: A review0Insights into Nanodiamond from Machine Learning1Phonon DOS‐Based Machine Learning Model for Designing High‐Performance Solid Electrolytes in Li‐Ion Batteries8Prediction of field dependent damping force as a function of magnetorheological damper design parameters using a machine learning method1Prediction of refractive index of 1-butyl-1-methylpyrrolidinium dicyanamide and water ionic liquid mixture: An application of machine learning1Beyond Combinatorial Materials Science: The 100 Prisoners Problem9Theory-Guided Feature Selection in Cybercrime Data Science1Reptation theory-similar deep learning model for polymer characterization from rheological measurement4Evaluating nano-metal oxide mixed matrix membranes for whey protein separation using hybrid intelligent optimization learning6Utilizing machine learning and phonon density of states for innovative approaches to design and optimize high-performance solid-state Mg-ion electrolytes10Efficient analysis of composites manufacturing using multi-fidelity simulation and probabilistic machine learning20A causal physics-informed deep learning formulation for groundwater flow modeling and climate change effect analysis19Physics-guided neural network for grinding temperature prediction12An efficient shape-based procedure for strain hardening identification in the post-necking phase5A critical reflection on attempts to machine-learn materials synthesis insights from text-mined literature recipes16Enhanced Real-Time ATM-Based MPC for Electric Vehicles With Cyber–Physical Security Aspect6Advances in machine learning-enhanced nanozymes24Deep Learning and Machine Learning for Materials Design3Review of machine learning applications for defect detection in composite materials25A review on the role of various machine learning algorithms in microwave-assisted pyrolysis of lignocellulosic biomass waste13Prediction and analysis of strength and economic feasibility of filling materials under the influence of mix proportion and curing age0Machine‐Learned Modeling for Accelerating Organic Solvent Design in Metal‐Ion Batteries2An interpretable predictive framework based on rules and machine learning: rule extraction, validation and adaptive integration with machine learning0Machine learning prediction of materials properties from chemical composition: Status and prospects29Physics informed neural network modelling for storm surge forecasting — A case study in the Bohai Sea, China37

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