Susanta Ghosh, an assistant professor in the Department of Mechanical and Aerospace Engineering, is co-author on a paper titled Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning, which was published August 12 in npj Computational Materials.
Ghosh and his then-PhD students Shashank Pathrudkar and Ponkrshnan Thiagarajan collaborated with Shivang Agarwal and Amartya S. Banerjee of UCLA to develop a new machine learning model that aims to break the cubic scaling barrier of quantum mechanics. The findings, detailed in the paper, are a result of research conducted by Ghosh’s group at Michigan Tech and Banerjee’s at UCLA.
Ghosh, who heads the Computational Science and Machine Learning Lab at Michigan Tech, served as faculty advisor for both Thiagarajan and Pathrudkar. Thiagarajan is currently a postdoctoral fellow at John Hopkins University, and Pathrudkar is now a senior engineer at MathWorks.
More information on the research will be featured in the upcoming issue of MAE magazine, the annual publication highlighting notable news and developments in the Michigan Tech College of Engineering’s Department of Mechanical and Aerospace Engineering.
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