Multi-objective Bayesian materials discovery: Application on the discovery of precipitation strengthened NiTi shape memory alloys through micromechanical modeling

Abstract

In this study, a framework for the multi-objective materials discovery based on Bayesian approaches is developed. The capabilities of the framework are demonstrated on an example case related to the discovery of precipitation strengthened NiTi shape memory alloys with up to three desired properties. In the presented case the framework is used to carry out an efficient search of the shape memory alloys with desired properties while minimizing the required number of computational experiments. The developed scheme features a Bayesian optimal experimental design process that operates in a closed loop. A Gaussian process regression model is utilized in the framework to emulate the response and uncertainty of the physical/computational data while the sequential exploration of the materials design space is carried out by using an optimal policy based on the expected hyper-volume improvement acquisition function. This scalar metric provides a measure of the utility of querying the materials design space at different locations, irrespective of the number of objectives in the performed task. The framework is deployed for the determination of the composition and microstructure of precipitation-strengthened NiTi shape memory alloys with desired properties, while the materials response as a function of microstructure is determined through a thermodynamically-consistent micromechanical model.

Data Assimilation in the Welding Process for Analysis of Weld Toe Geometry and Heat Source Model

“…The data assimilation is defined as a method of creating or improving a model by combining numerical simulations and observation data, and more narrowly refers to the assimilation of time series data and time evolution model in meteorology and seismology. In materials engineering, data assimilation is beginning to be used to estimate parameters such as the thermal conductivity in heat transfer analysis, 23) parameters in micromechanical modeling, 24,25) and the mobility of the phase-field simulation. [26][27][28] The present study aims to propose automatic characterization methods of weld toe geometry and heat source model by data assimilation techniques.…”

Rocketsled: a software library for optimizing high-throughput computational searches

“…The growing toolkit for running high-throughput calculations [39][40][41][42], the potentially immense search space (e.g. often more than 10 10 viable compounds [43]), and the growing number of studies using adaptive design in the materials domain to guide both simulations [34,[44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59] as well as experiments [44,[60][61][62][63] makes computational materials design an exciting field to explore with Rocketsled. In the two following case studies, we demonstrate the applicability of Rocketsled to adaptive design for materials discovery.…”

Experiment Design Frameworks for Accelerated Discovery of Targeted Materials Across Scales

“…To bypass this challenge, the current focus of the field is on the use of data to knowledge approaches, the idea being to implicitly extract the material physics embedded in the data itself with the use of modern day tools-machine learning, design optimization, manufacturing scale-up and automation, multiscale modeling, and uncertainty quantification with verification and validation. Typical techniques include the utilization of High-Throughput (HT) computational (Strasser et al, 2003;Curtarolo et al, 2013;Kirklin et al, 2013) and experimental frameworks (Strasser et al, 2003;Potyrailo et al, 2011;Suram et al, 2015;Green et al, 2017), which are used to generate large databases of materials feature / response sets, which then must be analyzed (Curtarolo et al, 2003) to identify the materials with the desired characteristics (Solomou et al, 2018). HT methods, however, fail to account for constraints in experimental / computational) resources available, nor do they anticipate the existence of bottle necks in the scientific workflow that unfortunately render impossible the parallel execution of specific experimental / computational tasks.…”

“…A Multi-objective Optimal Experiment Design (OED) framework (see Figure 4) based on the Bayesian optimization techniques was reported by the authors in Solomou et al (2018). The material to be optimized was selected to be precipitation strengthened NiTi Shape memory alloys (SMAs) since complex thermodynamic and kinetic modeling is necessary to describe the characteristics of these alloys.…”

“…Autonomous closed-loop, multi-objective Bayesian Optimal Experimental Design framework. Adapted fromSolomou et al (2018).…”