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RESEARCH PAPER · npj Computational Materials · 2018

A strategy to apply machine learning to small datasets in materials science

Ying Zhang, Chen Ling

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A strategy to apply machine learning to small datasets in materials scienceOriginalActive learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution902Integrated computational materials engineering for advanced materials: A brief review182A digital twin for rapid qualification of 3D printed metallic components339Recent Advances in Metallic Glass Nanostructures: Synthesis Strategies and Electrocatalytic Applications84Learning to predict single-wall carbon nanotube-recognition DNA sequences48Band gap and band alignment prediction of nitride-based semiconductors using machine learning85Making use of available and emerging data to predict the hazards of engineered nanomaterials by means of in silico tools: A critical review74Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment Amines112Predicting the polyethylene wear rate in pin-on-disc experiments in the context of prosthetic hip implants: Deriving a data-driven model using machine learning methods66Materials informatics: From the atomic-level to the continuum191Prediction of Synthesis of 2D Metal Carbides and Nitrides (MXenes) and Their Precursors with Positive and Unlabeled Machine Learning346Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia78Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries199Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data53Machine learning in energy economics and finance: A review436Auto-encoder-based generative models for data augmentation on regression problems67Machine Learning Enabled Prediction of Mechanical Properties of Tungsten Disulfide Monolayer60First-principles-based multiscale modelling of heterogeneous catalysis339Artificial intelligence for materials discovery120Error assessment and optimal cross-validation approaches in machine learning applied to impurity diffusion76Machine learning models for the lattice thermal conductivity prediction of inorganic materials166Recent advances and applications of machine learning in solid-state materials science2607Phillips-Inspired Machine Learning for Band Gap and Exciton Binding Energy Prediction55Autonomous Discovery in the Chemical Sciences Part II: Outlook245Dealing with small sample size problems in process industry using virtual sample generation: a Kriging-based approach49Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery152A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor70Predicting charge density distribution of materials using a local-environment-based graph convolutional network65Machine Learning Approaches for Thermoelectric Materials Research231Unsupervised discovery of solid-state lithium ion conductors312Enabling Catalyst Discovery through Machine Learning and High-Throughput Experimentation133Graph Convolutional Neural Networks as “General-Purpose” Property Predictors: The Universality and Limits of Applicability93Quantitative structure-property relationship (QSPR) analysis of calcium aluminosilicate glasses based on molecular dynamics simulations50Directly predicting limiting potentials from easily obtainable physical properties of graphene-supported single-atom electrocatalysts by machine learning173High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery179Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials50propnet: A Knowledge Graph for Materials Science74Applying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processes87The combined influence of grain size distribution and dislocation density on hardness of interstitial free steel91Boosting Tree-Assisted Multitask Deep Learning for Small Scientific Datasets111A Critical Review of Machine Learning of Energy Materials606Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm82Predicting the band gap of ZnO quantum dots via supervised machine learning models52Trends in 3D Printing Processes for Biomedical Field: Opportunities and Challenges210Molecular networks in Network Medicine: Development and applications211Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery99Machine learning in materials genome initiative: A review231Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning341Electronic Structure Modeling of Metal–Organic Frameworks329Machine learning prediction of compressive strength for phase change materials integrated cementitious composites240Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials57Development of Vickers hardness prediction models via microstructural analysis and machine learning50Machine learning approaches for elucidating the biological effects of natural products94Predicting Tensile Properties of AZ31 Magnesium Alloys by Machine Learning66Engineering early prediction of supercapacitors’ cycle life using neural networks50Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm387A machine learning framework to predict local strain distribution and the evolution of plastic anisotropy & fracture in additively manufactured alloys148Deep learning-based data augmentation method and signature verification system for offline handwritten signature56Learning quality characteristics for plastic injection molding processes using a combination of simulated and measured data62Assessing the uncertainty of maize yield without nitrogen fertilization55Engineered two-dimensional nanomaterials: an emerging paradigm for water purification and monitoring178Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies204Machine learning for high performance organic solar cells: current scenario and future prospects375Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm246Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects86Machine-learning-assisted low dielectric constant polymer discovery49Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining82A general and transferable deep learning framework for predicting phase formation in materials93Transfer learning for materials informatics using crystal graph convolutional neural network96Machine learning for material characterization with an application for predicting mechanical properties139Machine learning in materials science: From explainable predictions to autonomous design281Machine Learning for Transition-Metal-Based Hydrogen Generation Electrocatalysts122Machine learning and deep learning2642Optimization of nanofluid heat transfer in a microchannel heat sink with multiple synthetic jets based on CFD-DPM and MLA77Machine Learning Boosts the Design and Discovery of Nanomaterials138Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation240Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction241Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information49Structure prediction in high-entropy alloys with machine learning48Environmental Factors Associated With Nitrogen Fixation Prediction in Soybean49Deep learning method for predicting the mechanical properties of aluminum alloys with small data sets52A new ANN based crystal plasticity model for FCC materials and its application to non-monotonic strain paths115Computational discovery of energy materials in the era of big data and machine learning: A critical review60Accelerated discovery of single-phase refractory high entropy alloys assisted by machine learning98Predictive Design Model for Low-Dimensional Organic–Inorganic Halide Perovskites Assisted by Machine Learning114Predicting traffic demand during hurricane evacuation using Real-time data from transportation systems and social media82Machine learning and finite element analysis: An integrated approach for fatigue lifetime prediction of adhesively bonded joints47Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning50ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science153A review of data-driven modelling in drinking water treatment60Machine learning on small size samples: A synthetic knowledge synthesis258Fatigue life prediction of aluminum alloy via knowledge-based machine learning149Machine Learning Guided Synthesis of Flash Graphene110Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design59A review of the recent progress in battery informatics178A machine learning–based classification approach for phase diagram prediction51Application of machine learning for advanced material prediction and design123Perspective: Predicting and optimizing thermal transport properties with machine learning methods54Accuracy of predictions made by machine learned models for biocrude yields obtained from hydrothermal liquefaction of organic wastes53Machine learning in energy storage materials106Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys105Data‐Driven Materials Innovation and Applications187Materials Data toward Machine Learning: Advances and Challenges62Electropolymerized Molecularly Imprinted Polymer Synthesis Guided by an Integrated Data-Driven Framework for Cortisol Detection71Machine learning predicts electrospray particle size58A novel plant disease prediction model based on thermal images using modified deep convolutional neural network57Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability127A convolutional neural network based crystal plasticity finite element framework to predict localised deformation in metals115Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review295Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review97Machine learning algorithms in the environmental corrosion evaluation of reinforced concrete structures - A review86Machine learning for battery research110Overview: recent studies of machine learning in phase prediction of high entropy alloys56A Multidisciplinary Investigation into the Talent Development Processes at an English Football Academy: A Machine Learning Approach52Benchmarking AutoML for regression tasks on small tabular data in materials design48Customized development of promising Cu-Cr-Ni-Co-Si alloys enabled by integrated machine learning and characterization69Design of concrete incorporating microencapsulated phase change materials for clean energy: A ternary machine learning approach based on generative adversarial networks63Applications of contemporary artificial intelligence technology in forensic odontology as primary forensic identifier: A scoping review57Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions173Machine learning for algal biofuels: a critical review and perspective for the future60Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges66Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study97A generative deep learning framework for inverse design of compositionally complex bulk metallic glasses66Advancing oral delivery of biologics: Machine learning predicts peptide stability in the gastrointestinal tract53Optimization of operational strategy for ice thermal energy storage in a district cooling system based on model predictive control65Machine learning based very-high-cycle fatigue life prediction of AlSi10Mg alloy fabricated by selective laser melting77Data-driven electrolyte design for lithium metal anodes177Human–machine collaboration for improving semiconductor process development138Fatigue life analysis of high-strength bolts based on machine learning method and SHapley Additive exPlanations (SHAP) approach59Data quantity governance for machine learning in materials science228Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivity48Accelerating the prediction of stable materials with machine learning104Scope of machine learning in materials research—A review230The design and optimization of heterogeneous catalysts using computational methods61High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications84Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning70Machine Learning Design for High-Entropy Alloys: Models and Algorithms53Machine Learning Aided Design and Optimization of Thermal Metamaterials195Machine intelligence accelerated design of conductive MXene aerogels with programmable properties72The interface of machine learning and carbon quantum dots: From coordinated innovative synthesis to practical application in water control and electrochemistry88From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design190Machine learning-assisted optimization and evaluation of methylene blue adsorption kinetics on citrus aurantifolia leaves: Insights from isotherm and thermodynamic studies49Predicting fatigue life of multi-defect materials using the fracture mechanics-based physics-informed neural network framework62Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials188Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives92Deep learning-driven methods for network-based intrusion detection systems: A systematic review113Machine Learning in Polymer Research167Applications of density functional theory and machine learning in nanomaterials: A review51Artificial Intelligence‐Driven Development in Rechargeable Battery Materials: Progress, Challenges, and Future Perspectives53

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