Machine learning accurately predicts metallic defects

Dominant defect type predictions from r-MART model for 946 B2-type intermetallics. Colors indicates the relationship between prediction and calculations as shown in the legend. Credit: Bharat Medasani, Berkeley Lab / PNNL
Researchers at the Lawrence Berkeley National Laboratory have built and trained machine learning algorithms to predict defect behaviour in certain intermetallic compounds with high accuracy. They say this method will accelerate research of advanced alloys and lightweight materials for applications in automotive and aerospace.

Traditionally, researchers have used a computational quantum mechanical method known as density functional calculations to predict what kinds of defects can be formed in a given structure and how they affect the material's properties.

“Density functional calculations work well if you are modelling one small unit, but if you want to make your modelling cell bigger the computational power required to do this increases substantially,” explained Bharat Medasani, a former Berkeley Lab postdoc. “And because it is computationally expensive to model defects in a single material, doing this kind of brute force modelling for tens of thousands of materials is not feasible.”

To overcome these computing challenges, Medasani and his colleagues developed and trained machine learning algorithms to predict point defects in intermetallic compounds, focusing on the widely observed B2 crystal structure. Initially, they selected a sample of 100 of these compounds from the Materials Project Database and ran density functional calculations on supercomputers at the National Energy Research Scientific Computing Center (NERSC) to identify their defects.

Because they had a small data sample to work from, Medasani and his team used a forest approach called gradient boosting to develop their machine learning method to a high accuracy. In this approach additional machine learning models were built successively and combined with prior models to minimise the difference between the models predictions and the results from density functional calculations. The researchers repeated the process until they achieved a high level of accuracy in their predictions.

“The benefit of this work is now we have a computationally inexpensive machine learning approach that can quickly and accurately predict point defects in new intermetallic materials” said Andrew Canning, a Berkeley Lab Computational Scientist. “We no longer have to run very costly first principle calculations to identify defect properties for every new metallic compound.”

Author
Tom Austin-Morgan

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