Modeling Molecular Complexity: Building a Novel Multidisciplinary Machine Learning Framework to Understand Molecular Synthesis and Signatures
Jaden J. A. Hastings, Weill Cornell Medicine, Dept of Biophysics and Physiology, New York, United States, Aaron C Bell, The University of Tokyo, Tokyo, Japan, Timothy Gebhard, Max Planck Institute for Intelligent Systems, Empirical Inference, Tübingen, Germany, Jian Gong, Massachusetts Institute of Technology, Earth, Atmospheric, and Planetary Sciences, Cambridge, MA, United States, Atılım Güneş Baydin, University of Oxford, Department of Engineering Science, Oxford, United Kingdom, Matthew Fricke, University of New Mexico, Albuquerque, United States, Massimo Mascaro, Google, Applied AI, Mountain View, United States, Michael Phillips, University of Northern Colorado, School of Psychological Sciences, Greeley, CO, United States, Kimberly Warren-Rhodes, SETI Institute Mountain View, Mountain View, United States and Nathalie A Cabrol, SETI Institute, Mountain View, CA, United States
