Artificial Intelligence Meets Materials Science

Estimated reading time: 5 minutes

The algorithm represents a smarter step forward compared to previous work in the field. Other algorithms force one to start with a predefined model, which introduces a constraint into the experiment and can skew the results. “Our algorithm can automatically and autonomously decide which model is the best model out of n models, at any given time, depending on the acquired data,” said Talapatra. The autonomous computer program reduces the number of steps and limits the use of limited resources. Since it can start with as few as two experiments as initial data points, the algorithm is ideal for optimizing initial experiments and discerning the best path forward.

It can be used as a one-step tool by experimentalists to simply decide on the next material to explore, or as a purely computational tool to replace expensive computational models and reduce computational costs. It can also be used in a combined experimental and computational setup.  At the very least, this framework provides a very efficient means of building the initial data set since it may be used to guide experiments or calculations by focusing on gathering data in those sections of the materials design space which will result in the most efficient path to achieving the optimal material.

“Typically, materials research occurs in a very ad-hoc way and serendipity tends to be the rule, rather than the exception,” said Talapatra. “The problem is you often don’t know the fundamental physics behind why a material is or is not working. Our models are not precise enough. When you start a materials discovery journey, you start with the very basic physical knowledge, such as the number of electrons and what happens when the elements join together. You have to find the similarities between the features and the properties.”

“We included as much science as possible in the (artificial intelligence) models,” said Boluki, a doctoral student who will defend his thesis next fall. Boluki and Talapatra worked as implementers in the project and coded it in python together.

The paper on the algorithm has been peer reviewed, presented at several conferences and given good feedback from the materials science and engineering community. Engineers and scientists at Texas A&M are already using the program.

From Cell Pathology to Materials Science: The Mathematical Underpinning

In 2011, Qian and Dougherty began collaborating on enhancing experiment design in biomedical research. They utilized mathematical models to see when cells are going to the tumor stage.

That same year, federal policymakers announced the Materials Genome Initiative, which aims to accelerate the discovery of new advanced materials by combining the use of computational and experimental tools along with digital data. Over the last eight years, nationwide, much time, money and resources have been invested in this effort.

Qian and Dougherty turned their focus to materials science problems in 2013. The team started working on optimal design problems two years ago, initially collaborating with Drs. Turab Lookman and Prasanna Balachandran from Los Alamos National Laboratory. Current paradigms are typically centered around the idea of exploring the materials space through experimentation or computation and their approach showed that there are more efficient ways of discovering materials.

“While other people were focusing on the generation and analysis of huge amounts of data, we realized that the best way forward was to focus on experiment design — how to explore the vast domain of possible materials and increase our chances of success by choosing materials with a goal, target property, or response in mind,” said Talapatra.

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