.png){kind=icon}
返回
An active learning high-throughput microstructure calibration framework for solving inverse structure-process problems in materials informatics
.png){kind=icon}
OA
AI
DOI：10.1016/j.actamat.2020.04.054.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Determining a process-structure-property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process-property linkage by bypassing the materials structure, or in the context of structure-property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process-structure linkage, which has a great implication in reverse engineering. In this work, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization to exploit high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered. (C) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Keyword:
Microstructure descriptors
Bayesian optimization
Process-structure
Additive manufacturing
Grain growth
Kinetic Monte Carlo
ICME
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.3
论文数:
2.0W
被引数:
12.9W
机构
引用论文
Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review使用元启发式和机器学习进行并行计算系统的软件优化: 系统的文献综述.png)
COMPUTINGOAAI
COMPUTINGIF2.8
OAAI分享收藏Data-driven high-fidelity 2D microstructure reconstruction via non-local patch-based image inpaintingACTA MATERIALIAOAAI
ACTA MATERIALIAIF9.3
OAAI分享收藏Microstructure-based knowledge systems for capturing process-structure evolution linkages基于微观结构的知识系统，用于捕获过程-结构演化联系OAAI
OAAI分享收藏Schizophrenia with Comorbid Idiopathic Parkinson's Disease: A Difficult Clinical Management ScenarioOAAI
OAAI分享收藏
查看更多内容