Members of the research community at Microsoft work continuously to advance their respective fields. Abstracts brings its audience to the cutting edge with them through short, compelling conversations about new and noteworthy achievements. In this episode, Senior Researcher Chang Liu joins host Gretchen Huizinga to discuss <a href="https://www.microsoft.com/en-us/research/publication/overcoming-the-barrier-of-orbital-free-density-functional-theory-for-molecular-systems-using-deep-learning/">Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning</a>.” In the paper, Liu and his coauthors present M-OFDFT, a variation of orbital-free density functional theory (OFDFT). M-OFDFT leverages deep learning to help identify molecular properties in a way that minimizes the tradeoff between accuracy and efficiency, work with the potential to benefit areas such as drug discovery and materials discovery.<a href="https://www.microsoft.com/en-us/research/publication/overcoming-the-barrier-of-orbital-free-density-functional-theory-for-molecular-systems-using-deep-learning/">Read the paper</a>

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Abstracts: March 21, 2024

Microsoft Research Podcast