Workshop on deep learning for multiphase chemistry
September 27-28, 2018
Beckman Center of the National Academy of Sciences at the Univ. of California, Irvine.
Accurate prediction of the future given a set of chemical reactants is a desirable capability in many fields. Multiphase environmental organic chemistry deals with the physicochemical transformations between gaseous, liquid and solid matter on scales ranging from nanoseconds to millennia, subatomic particles to solar systems. Artificial intelligence and machine learning lend themselves to chemical discovery in this area, in part, because traditional approaches are difficult when systems are highly non-linear and the probabilistic nature is non-trivial. Evolution of organic compounds in multiphase systems is complex as there are simultaneous sequential and parallel chemical corridors in addition to phase transitions. While the field has some understanding of the multiphase organic phenomena in relatively simple systems, little is known under realistic complex conditions. The controlling electrochemical details are relatively unknown and there is limited understanding of fundamental controlling factors of radical-driven chemistry in thin films when complex, often highly concentrated and non-ideal mixtures of water, organics and inorganics are present.
Sample science questions to be addressed:
1.) What are the best strategies to gather and organize multiphase chemical mechanism data in sufficient quantities suitable for AI applications?
2.) What are multiphase chemistry questions currently unanswerable for which application of AI may facilitate breakthrough?
Organizing Committee: Annmarie Carlton, Pierre Baldi
Attendee Travel: The venue is conveniently located (e.g., 15 minutes from the Santa Ana airport). A suite of rooms is booked at the Ayres Hotel in Costa Mesa/Newport Beach, best reached by Uber/Lyft from SNA. We have arranged for a shuttle service to-and-from the hotel to the Beckman center. More details to follow.
Agenda and Attendees: TBD