Machine learning-assisted high-throughput exploration of interface energy space in multi-phase-field model with CALPHAD potential

Materials Theory

Vahid Attari¹  Raymundo Arroyave² 

[1] Materials Science and Engineering Department, Texas A&M University, 77840, College Station, TX, USA;Materials Science and Engineering Department, Texas A&M University, 77840, College Station, TX, USA;Mechanical Engineering Department, Texas A&M University, 77840, College Station, TX, USA;

关键词: Microstructure-sensitive materials design;    Interface energy;    Phase-field modeling;    Uncertainty propagation;    Global sensitivity analysis;    Semi-supervised machine learning;    Variational Autoencoder;   

DOI  :  10.1186/s41313-021-00038-0

来源: Springer

【 摘 要 】

Computational methods are increasingly being incorporated into the exploitation of microstructure–property relationships for microstructure-sensitive design of materials. In the present work, we propose non-intrusive materials informatics methods for the high-throughput exploration and analysis of a synthetic microstructure space using a machine learning-reinforced multi-phase-field modeling scheme. We specifically study the interface energy space as one of the most uncertain inputs in phase-field modeling and its impact on the shape and contact angle of a growing phase during heterogeneous solidification of secondary phase between solid and liquid phases. We evaluate and discuss methods for the study of sensitivity and propagation of uncertainty in these input parameters as reflected on the shape of the Cu6Sn5 intermetallic during growth over the Cu substrate inside the liquid Sn solder due to uncertain interface energies. The sensitivity results rank σSI,σIL, and σIL, respectively, as the most influential parameters on the shape of the intermetallic. Furthermore, we use variational autoencoder, a deep generative neural network method, and label spreading, a semi-supervised machine learning method for establishing correlations between inputs of outputs of the computational model. We clustered the microstructures into three categories (“wetting”, “dewetting”, and “invariant”) using the label spreading method and compared it with the trend observed in the Young-Laplace equation. On the other hand, a structure map in the interface energy space is developed that shows σSI and σSL alter the shape of the intermetallic synchronously where an increase in the latter and decrease in the former changes the shape from dewetting structures to wetting structures. The study shows that the machine learning-reinforced phase-field method is a convenient approach to analyze microstructure design space in the framework of the ICME.

【 授权许可】

CC BY