Accelerated materials design using batch Bayesian optimization: A case study for solving the inverse problem from materials microstructure to process specification-Reference-Cited by-同舟云学术

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

Published:2022-07 Issue: Volume:210 Page:111417
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Honarmandi P.^ORCID,Attari V.,Arroyave R.

Publisher

Elsevier BV

Subject

Computational Mathematics,General Physics and Astronomy,Mechanics of Materials,General Materials Science,General Chemistry,General Computer Science

Reference56 articles.

1. An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics;Tran;Acta Mater.,2020

2. Optimal experimental design for materials discovery;Dehghannasiri;Comput. Mater. Sci.,2017

3. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design;Lookman;NPJ Comput. Mater.,2019

4. Autonomous efficient experiment design for materials discovery with Bayesian model averaging;Talapatra;Phys. Rev. Mater.,2018

5. Efficient global optimization of expensive black-box functions;Jones;J. Global Optim.,1998

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Discovery of novel materials through machine learning;Journal of Physics: Condensed Matter;2024-08-14

2. Optimized design of composition and brazing process for Cu-Ag-Zn-Mn-Ni-Si-B-P alloy brazing material based on machine learning strategy to improve brazing properties;Materials Today Communications;2024-06

3. Microstructure-Sensitive Deformation Modeling and Materials Design with Physics-Informed Neural Networks;AIAA Journal;2024-05

4. Efficient searching of processing parameter space to enable inverse microstructural design of materials;Acta Materialia;2024-01

5. Minimizing the weight uncertainty in Neural Networks Using Bayesian Optimization;2023 7th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech);2023-12-18