Session: Theory, Data Science, and Machine Learning for Electronic and Photonic Materials

Theory, data science, and machine learning have grown as crucial research pillars for material discovery. They also enable fundamental insights necessary to correlate a material’s nanoscale structure and chemistry with its macroscopic properties and streamlined materials research. Submissions are invited on, but not limited to, theoretical, computational, data science and machine learning-driven research approaches to understand existing materials and discover novel materials. Studies that incorporate one or more research pillars are particularly encouraged. Areas of interest include, electronics, photonics, optoelectronics, energy storage/production/conversion, (electro)chemical systems, and quantum information science.