Starts 11 Jun 2020 15:00
Ends 11 Jun 2020 16:30
Central European Time
Search
Search in Conferences:
CMSP Seminar (News and Views): Topology, Molecular Simulation and Machine Learning as Routes to Exploring Structure and Phase Behavior in Molecular and Atomic Crystals
Virtual
Registration Instructions:
- If you have already registered for a previous CMSP seminar there is no need to register again. Please remember to use the link within the email you received after the registration to join the webinar.
- If you have NOT registered before, please make sure to register several days in advance before the webinar to the Seminar Series by clicking here.
- If you have already registered for a previous CMSP seminar there is no need to register again. Please remember to use the link within the email you received after the registration to join the webinar.
- If you have NOT registered before, please make sure to register several days in advance before the webinar to the Seminar Series by clicking here.
Abstract:
Organic molecular crystals frequently exist in multiple forms known as polymorphs. Structural differences between crystal polymorphs can affect desired properties, such as bioavailability of active pharmaceutical formulations, lethality of pesticides, or electrical conductivity of organic semiconductors. Crystallization conditions can influence polymorph selection, making an experimentally driven hunt for polymorphs difficult. Such efforts are further complicated when polymorphs initially obtained under a particular experimental protocol “disappear” in favor of another polymorph in subsequent repetitions of the experiment. Consequently, theory and computational can potentially play a vital role in mapping the landscape of crystal polymorphism. Traditional crystal structure prediction methods face their own challenges, and therefore, new approaches are needed. In this talk, I will show, by leveraging concepts from mathematics, specifically geometry and topology, and statistical mechanics in combination with techniques of molecular simulation, traditional methods, and machine learning, that a new paradigm in crystal structure prediction may be emerging. Examples demonstrating prediction of structures of crystals, co-crystals, and phase transitions will be presented.
