Accelerated materials design using batch Bayesian optimization: A case study for solving the inverse problem from materials microstructure to process specification

[1]  R. Arróyave,et al.  Machine learning-assisted high-throughput exploration of interface energy space in multi-phase-field model with CALPHAD potential , 2022, Materials Theory.

[2]  Sofia Z. Sheikh,et al.  Microstructure Classification in the Unsupervised Context , 2021, SSRN Electronic Journal.

[3]  P. Voorhees,et al.  Segmentation of Experimental Datasets Via Convolutional Neural Networks Trained on Phase Field Simulations , 2021, Acta Materialia.

[4]  J. A. Stewart,et al.  A data-driven surrogate model to rapidly predict microstructure morphology during physical vapor deposition , 2020 .

[5]  T. Dhaene,et al.  Compact representations of microstructure images using triplet networks , 2020, npj Computational Materials.

[6]  Anh Tran,et al.  An active learning high-throughput microstructure calibration framework for solving inverse structure-process problems in materials informatics , 2020, ArXiv.

[7]  Santu Rana,et al.  Bayesian Optimization for Adaptive Experimental Design: A Review , 2020, IEEE Access.

[8]  U. Braga-Neto,et al.  Semi-Supervised Learning Approaches to Class Assignment in Ambiguous Microstructures , 2019, Acta Materialia.

[9]  Wei Chen,et al.  Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables , 2019, Scientific Reports.

[10]  Douglas Allaire,et al.  Uncertainty Propagation in a Multiscale CALPHAD-Reinforced Elastochemical Phase-Field Model , 2019, Acta Materialia.

[11]  Wei Chen,et al.  Data-Centric Mixed-Variable Bayesian Optimization For Materials Design , 2019, Volume 2A: 45th Design Automation Conference.

[12]  Turab Lookman,et al.  Machine learning assisted design of high entropy alloys with desired property , 2019, Acta Materialia.

[13]  Dimitris C. Lagoudas,et al.  Experiment Design Frameworks for Accelerated Discovery of Targeted Materials Across Scales , 2019, Front. Mater..

[14]  Turab Lookman,et al.  Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design , 2019, npj Computational Materials.

[15]  Atsushi Sakurai,et al.  Ultranarrow-Band Wavelength-Selective Thermal Emission with Aperiodic Multilayered Metamaterials Designed by Bayesian Optimization , 2019, ACS central science.

[16]  A. Cruzado,et al.  Exploration of the Microstructure Space in Tialzrn Ultra-Hard Nanostructured Coatings , 2018, Acta Materialia.

[17]  Jianfei Cai,et al.  Understanding and Comparing Scalable Gaussian Process Regression for Big Data , 2018, Knowl. Based Syst..

[18]  Carolyn Conner Seepersad,et al.  Design of Mechanical Metamaterials via Constrained Bayesian Optimization , 2018, Volume 2A: 44th Design Automation Conference.

[19]  Junichiro Shiomi,et al.  Multifunctional structural design of graphene thermoelectrics by Bayesian optimization , 2018, Science Advances.

[20]  T. Lookman,et al.  Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning , 2018, Nature Communications.

[21]  Xiaoning Qian,et al.  Autonomous efficient experiment design for materials discovery with Bayesian model averaging , 2018, Physical Review Materials.

[22]  T. Lookman,et al.  Accelerated Discovery of Large Electrostrains in BaTiO3‐Based Piezoelectrics Using Active Learning , 2018, Advanced materials.

[23]  Cheng Li,et al.  Rapid Bayesian optimisation for synthesis of short polymer fiber materials , 2017, Scientific Reports.

[24]  J. Hogden,et al.  Statistical inference and adaptive design for materials discovery , 2017 .

[25]  Edward R. Dougherty,et al.  Optimal experimental design for materials discovery , 2017 .

[26]  Junichiro Shiomi,et al.  Designing Nanostructures for Phonon Transport via Bayesian Optimization , 2016, 1609.04972.

[27]  Kipton Barros,et al.  Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning , 2016, Scientific Reports.

[28]  Elizabeth A. Holm,et al.  A computer vision approach for automated analysis and classification of microstructural image data , 2015 .

[29]  A. Karma,et al.  Topology-generating interfacial pattern formation during liquid metal dealloying , 2015, Nature Communications.

[30]  Heping Chen,et al.  Welding parameter optimization based on Gaussian process regression Bayesian optimization algorithm , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[31]  Atsuto Seko,et al.  Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization. , 2015, Physical review letters.

[32]  Neil D. Lawrence,et al.  Batch Bayesian Optimization via Local Penalization , 2015, AISTATS.

[33]  Yong Du,et al.  Structural, phonon and thermodynamic properties of fcc-based metal nitrides from first-principles calculations , 2012 .

[34]  Cv Clemens Verhoosel,et al.  A phase-field description of dynamic brittle fracture , 2012 .

[35]  Hae-Sang Park,et al.  A simple and fast algorithm for K-medoids clustering , 2009, Expert Syst. Appl..

[36]  B. Blanpain,et al.  An introduction to phase-field modeling of microstructure evolution , 2008 .

[37]  D. Fullwood,et al.  Microstructure reconstructions from 2-point statistics using phase-recovery algorithms , 2008 .

[38]  T. Abinandanan,et al.  Phase field study of precipitate rafting under a uniaxial stress , 2007 .

[39]  Yang Liu,et al.  Macroscopic properties and field fluctuations in model power-law polycrystals: full-field solutions versus self-consistent estimates , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[40]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[41]  Hervé Moulinec,et al.  A numerical method for computing the overall response of nonlinear composites with complex microstructure , 1998, ArXiv.

[42]  Jie Shen,et al.  Applications of semi-implicit Fourier-spectral method to phase field equations , 1998 .

[43]  P. Voorhees,et al.  Ostwald ripening in a system with a high volume fraction of coarsening phase , 1988, Metallurgical and Materials Transactions A.

[44]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[45]  Svetha Venkatesh,et al.  Batch Bayesian optimization using multi-scale search , 2020, Knowl. Based Syst..

[46]  Zi-kui Liu,et al.  A thermodynamic description of metastable c-TiAlZrN coatings with triple spinodally decomposed domains , 2017 .

[47]  Nando de Freitas,et al.  Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.

[48]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[49]  John W. Cahn,et al.  Phase Separation by Spinodal Decomposition in Isotropic Systems , 1965 .