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RESEARCH PAPER · Acta Materialia · 2017

An informatics approach to transformation temperatures of NiTi-based shape memory alloys

Dezhen Xue, Deqing Xue, Deqing Xue, Deqing Xue, Ruihao Yuan, Yumei Zhou, Prasanna V. Balachandran, Xiangdong Ding, Jun Feng Sun, Turab Lookman

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An informatics approach to transformation temperatures of NiTi-based shape memory alloysOriginalHigh-Dimensional Materials and Process Optimization Using Data-Driven Experimental Design with Well-Calibrated Uncertainty Estimates179An materials informatics approach to Ni-based single crystal superalloys lattice misfit prediction65Building data-driven models with microstructural images: Generalization and interpretability98Identifying models of dielectric breakdown strength from high-throughput data via genetic programming30Doping Effects of Point Defects in Shape Memory Alloys33Importance of Feature Selection in Machine Learning and Adaptive Design for Materials31Materials informatics167Multi-objective Optimization for Materials Discovery via Adaptive Design171Effects of Wire Electro-Discharge Machining Process Parameters on the Machined Surface of Ti50Ni49Co1 Shape Memory Alloy42Tunability of martensitic behavior through coherent nanoprecipitates and other nanostructures7Machine leaning aided study of sintered density in Cu-Al alloy30Two-way design of alloys for advanced ultra supercritical plants based on machine learning50Patchy Particle Model of the Hierarchical Self-Assembly of π-Conjugated Optoelectronic Peptides21The materials data ecosystem: Materials data science and its role in data-driven materials discovery6Machine learning in materials design and discovery: Examples from the present and suggestions for the future198Materials informatics: From the atomic-level to the continuum191A novel approach to predict green density by high-velocity compaction based on the materials informatics method8Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design832Machine learning assisted design of high entropy alloys with desired property936Systems Approaches to Materials Design: Past, Present, and Future79A two-stage predicting model for γ′ solvus temperature of L12-strengthened Co-base superalloys based on machine learning49Data analysis of multi-dimensional thermophysical properties of liquid substances based on clustering approach of machine learning13Machine Learning Techniques for the Segmentation of Tomographic Image Data of Functional Materials94Accelerated Search for BaTiO3‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design100Virtual Issue on Machine-Learning Discoveries in Materials Science29Nanocalorimetry and ab initio study of ternary elements in CuZr-based shape memory alloy10Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization3A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys285Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models461Wire electro spark machining and characterization studies on Ti50Ni49Co1, Ti50Ni45Co5 and Ti50Ni40Co10 alloys4A progressive learning method for predicting the band gap of ABO 3 perovskites using an instrumental variable50Bayesian Global Optimization applied to the design of shape-memory alloys6Cost-effective materials discovery: Bayesian optimization across multiple information sources38Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening310Learning with Small Databases0Navigating the design space of inorganic materials synthesis using statistical methods and machine learning43Accelerating the development of multi-component Cu-Al-based shape memory alloys with high elastocaloric property by machine learning37Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach148Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration0Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods10Development of database and information management system for data-driven materials design4Estimating DFT calculated voltage using machine learning regression models4Gallium–Boron–Phosphide ($\hbox {GaBP}_{2}$): a new III–V semiconductor for photovoltaics11Combinatorial Synthesis and High-Throughput Characterization of Microstructure and Phase Transformation in Ni–Ti–Cu–V Quaternary Thin-Film Library34Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches161Machine learning of mechanical properties of steels162A strategy combining machine learning and multiscale calculation to predict tensile strength for pearlitic steel wires with industrial data61Optimization of thermal conductivity of UO2–Mo composite with continuous Mo channel based on finite element method and machine learning25Machine learning assisted multi-objective optimization for materials processing parameters: A case study in Mg alloy119Predicting the Parabolic Rate Constants of High-Temperature Oxidation of Ti Alloys Using Machine Learning42Adaptive machine learning for efficient materials design31Machine learning assisted materials design and discovery for rechargeable batteries466Role of uncertainty estimation in accelerating materials development via active learning45Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization53Cluster-formula-embedded machine learning for design of multicomponent β-Ti alloys with low Young’s modulus78Rapid evaluation method for anisotropic growth of WS2 monolayers by combining machine learning algorithms and kinetic Monte Carlo simulation data5Accelerated design of L12-strengthened Co-base superalloys based on machine learning of experimental data44Machine learning in additive manufacturing: State-of-the-art and perspectives693An Informatic Approach to Predict the Mechanical Properties of Aluminum Alloys using Machine Learning Techniques26Artificial intelligence for photonics and photonic materials63Knowledge-Based Descriptor for the Compositional Dependence of the Phase Transition in BaTiO3-Based Ferroelectrics22Transformation Temperature Predictions Through Computational Intelligence for NiTi-Based Shape Memory Alloys101A screening strategy for hot forging combining high-throughput forging experiment and machine learning3Prediction of the NiTi shape memory alloy composition with the best corrosion resistance for dental applications utilizing artificial intelligence33Data analytics approach to predict the hardness of copper matrix composites5Machine learning-aided design of aluminum alloys with high performance66Determining Multi‐Component Phase Diagrams with Desired Characteristics Using Active Learning49Evaluating yield strength of Ni-based superalloys via high throughput experiment and machine learning20Physics-informed machine learning for composition – process – property design: Shape memory alloy demonstration131Data science applications for predictive maintenance and materials science in context to Industry 4.0127Deep Learning Approach for Chemistry and Processing History Prediction From Materials Microstructure: Application to Spinodal Decomposition1Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest8Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys259Efficient estimation of material property curves and surfaces via active learning24Accelerated Development of High-Strength Magnesium Alloys by Machine Learning46Data Science, Machine Learning and Artificial Intelligence Applied to Metals and Alloys Research: Past, Present, and Future3Machine learning in materials science: From explainable predictions to autonomous design281Machine learning of phases and mechanical properties in complex concentrated alloys162Machine learning identified materials descriptors for ferroelectricity110Efficient sampling for decision making in materials discovery*10Determination of glass forming ability of bulk metallic glasses based on machine learning58Machine learning assisted screening of non-rare-earth elements for Mg alloys with low stacking fault energy23A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys47Machine learning for alloys698Designing highly ductile magnesium alloys: current status and future challenges99Machine learning assisted empirical formula augmentation21Prediction of two-phase composite microstructure properties through deep learning of reduced dimensional structure-response data69Characterization of the Convoluted 3D Intermetallic Phases in a Recycled Al Alloy by Synchrotron X-ray Tomography and Machine Learning13Age-hardening behavior guided by the multi-objective evolutionary algorithm and machine learning22ASMADA—A tool for automatic analysis of shape memory alloy thermal cycling data under constant stress7DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions0DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions4Prediction of Aluminum Alloys Composition for Industrial Requirement Using Data Analysis Techniques2DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions3Computational materials design: Composition optimization to develop novel Ni-based single crystal superalloys26An Interpretable Boosting-Based Predictive Model for Transformation Temperatures of Shape Memory Alloys21Artificial Intelligence based Approach for Rapid Material Discovery: From Chemical Synthesis to Quantum Materials0DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions28Estimating the performance of a material in its service space via Bayesian active learning: a case study of the damping capacity of Mg alloys14NiTi shape memory alloys: properties16Accelerated design for elastocaloric performance in NiTi-based alloys through machine learning17Automated pipeline for superalloy data by text mining83Global‐Oriented Strategy for Searching Ultrastrength Martensitic Stainless Steels11A multiple loops machine learning framework to predict the properties of WC–Co based cemented carbides17Design of a NiTiHf shape memory alloy with an austenite finish temperature beyond 400 °C utilizing artificial intelligence35Magnetic performance oriented composition design of Sm-Co based alloys by machine learning and experimental studies22Data-driven shape memory alloy discovery using Artificial Intelligence Materials Selection (AIMS) framework76Landau theory of ferroelastic phase transitions: Application to martensitic phase transformations9A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation14Deep learning approach for chemistry and processing history prediction from materials microstructure22Machine Learning–Assisted Design of Material Properties80Recent progress in the machine learning-assisted rational design of alloys69Accelerated discovery of high-performance piezocatalyst in BaTiO3-based ceramics via machine learning84Prediction of Oxygen Evolution Activity for NiCoFe Oxide Catalysts via Machine Learning31Screening for shape memory alloys with narrow thermal hysteresis using combined XGBoost and DFT calculation18Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning44Design of Ni-based turbine disc superalloys with improved yield strength using machine learning18Machine learning-assisted design of biomedical high entropy alloys with low elastic modulus for orthopedic implants30Recent progress in nanocrystalline Sm–Co based magnets35Processing time, temperature, and initial chemical composition prediction from materials microstructure by deep network for multiple inputs and fused data17Exploration of the oxidation and ablation resistance of ultra-high-temperature ceramic coatings using machine learning36Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning91Design of super-hard high-entropy ceramics coatings via machine learning62Establishing reduced-order process-structure linkages from phase field simulations of dendritic grain growth during solidification6Using Machine Learning to Predict Oxygen Evolution Activity for Transition Metal Hydroxide Electrocatalysts39Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy7Machine-learning model for prediction of martensitic transformation temperature in NiMnSn-based ferromagnetic shape memory alloys37Low-hysteresis shape-memory ceramics designed by multimode modelling57Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys46A perspective on Bayesian methods applied to materials discovery and design47Machine Learning Captures Synthetic Intuitions for Hollow Nanostructures4Data-driven materials discovery and synthesis using machine learning methods14State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning9Machine learning accelerates the materials discovery142Deep Learning of Microstructures0Data-Driven Study of Shape Memory Behavior of Multi-Component Ni–Ti Alloys in Large Compositional and Processing Space26Machine learning assisted design of aluminum-lithium alloy with high specific modulus and specific strength56Interpretable machine learning workflow for evaluation of the transformation temperatures of TiZrHfNiCoCu high entropy shape memory alloys62Accelerated discovery of cost-effective Nd–Fe–B magnets through adaptive learning7Digital Model for Rapid Prediction of Die Forging Force and Forming Control of Aluminum Alloy Aviation Components0Discovery of Novel Photocatalysts Using Machine Learning Approach4Effect of Cu Content on the Martensitic Transformation and Shape Memory Behaviors in Ti31.5hf15zr5ni48.5-Xcux Alloys0Molecular dynamics simulation of the porosity effect on transformation mechanism of nanocrystalline porous NiTi shape memory alloy19Experimental Feedback to Improve the Extrapolation of Machine Learning: Application to Design of PM Cu–Al–Fe–Ni Alloys1Exploring the relationship between lattice distortion and phase stability in a multi-principal element alloy system based on machine learning method34Design Strategy for Art Copper Alloys’ Colors Through Machine Learning and Oxidation Treatment3Accelerated design of lead-free high-performance piezoelectric ceramics with high accuracy via machine learning33Improved material descriptors for bulk modulus in intermetallic compounds via machine learning27Machine learning superalloy microchemistry and creep strength from physical descriptors10Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel16

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