Study Uses Machine Learning to Predict Performance of New Material-The Panagora Blog

Defense system is one of the key factors that contribute in the progress of any country. There are many organizations such as Lawrence Livermore National Laboratory (LLNL) supporting nations to make defense systems stronger. LLNL is engaged in research and development activities. The key task of this organization is to discover and check usability of new materials required in defense activities. However, discovering a material and its actual deployment is a tedious task and may take years. Researchers at LLNL have discovered a new technique that might reduce this timing.

Deploying Advance Technology to Accelerate Deployment Process

Scientists stated that they have developed a technique that uses machine learning to aid in accelerating the development cycle. In turn, it helps in reducing time required for actual deployment of the new material. This research is accessible in the journal Materials and Design. In this research, the team focused on predicting properties of important material such as TATB—which has significant use in defense system—using machine learning.  They used combination of computer vision and machine learning, which use scanning electron microscopy (SEM) images. This helped them to avoid actual fabrication and testing part.

Scientists proved the possibility of training machine learning model to predict the material performance on the basis of SEM images. The technique offers 24% error reductions as compared to the present key techniques, which include instrument data and domain-expert assessment. Brian Gallagher is the lead author of this study. He stated, “Our motive is not only to precisely predict the performance of material, but to offer feedback to experimentalists to modify synthesis conditions to give higher-performance materials. These outcomes move us one step nearer to that motive.” Moreover, Yong Han, the corresponding author of this study, added, “Our work shows the usefulness of applying new machine-learning tactics to deal with critical materials science issues. We aim to expand on this work and deal with explainability, data sparsity, uncertainty, and development of domain-aware model.”