报告题目：Machine learning approaches to materials failure: From the atomic to the macroscale

报 告 人：Michael Zaiser  博导、教授

报告时间：2025年5月22日（星期四）15:00-16:30

报告地点：明德楼B308

报告人简介：毕业于德国斯图加特大学，获理论物理博士学位。历任英国爱丁堡大学讲师、教授，2012年起任德国埃尔朗根-纽伦堡大学材料系讲席教授（Chair Professor）。皇家永利“天府学者”，教育部“海外名师”。主要研究领域为微纳米材料力学及高性能材料，应用统计物理、材料科学、固体力学等理论，研究材料的微结构和缺陷的无序性和随机性，及对其材料宏观力学性能的影响。在Science，Nature Physics，Nature Communications，Advanced Energy Materials，Physical Review Letters等国际顶尖期刊上发表学术论文200余篇，累计引用达8000余次，出版专著2部，参与编写专著5部。

报告内容摘要：Materials failure is a complex spatio-temporal phenomenon involving processes that range from the interactions of single atoms and the breaking of atomic bonds to stress re-distribution and rupture on macroscopic and even geophysical scales. Machine learning approaches can help us to understand failure processes based on simulation and monitoring data, and to use this information in order to predict when, where and how materials fail. We illustrate this for two examples: (i) On the atomic scale, we show how a combination of classification and image recognition tools can be used to identify atomic environments that are prone to crack nucleation under load, to predict the critical loads for cracks to emerge, and  to forecast the ensuing crack paths; (ii) on the scale of macroscopic creep samples of metallic glasses, we show how regression tools can be developed that allow to predict, based on monitoring records, sample specific failure times, and we discuss the importance of establishing and exploiting spatial signatures of incipient failure to obtain accurate forecasts. Based on these examples, we discuss general issues related to feature selection and the importance of incorporating domain knowledge into machine learning approaches.

主办单位：

新能源与材料学院/光伏新能源现代产业学院

油气藏地质及开发工程全国重点实验室

科学技术发展研究院

成都新材料学会

氢能制取与高效利用重点实验室

成都市科技青年联合会材料能源专委会

能量转换与储存先进材料皇家永利国际科技合作基地

皇家永利页岩气高效开采先进材料制备技术工程研究中心

皇家永利玄武岩纤维复合材料开发及应用工程技术研究中心