Generative models for classical and quantum matter

You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/generative-models-for-classical-and-quantum-matter-1274.

Registration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any).  However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the CECAM Event Manager if you have any question.

Description
Generative modeling, a broad term that encompasses many machine learning techniques to generate random variables that resemble samples from a given target distribution, are being heavily explored in the computational molecular sciences. For example, pioneering works using these techniques in a chemical context include efforts to sample thermal distributions of molecular systems [1], enhance free energy methods [2], evaluate intractable integrals arising in quantum field theories [3], carrying out direct simulations, among many other applications. The main paradigms that have been exploited in the molecular sciences include likelihood estimation with normalizing flows (i.e., training from knowledge of the energy alone) or training from an existing dataset, typically collected using molecular dynamics simulations [4]. At present, it is not clear whether substantive accelerations relative to explicit simulations can be obtained for every system [5]. 
Indeed, while there is palpable excitement about generative modeling techniques, a number of determinative questions remain:
 

• How do we build generative models for chemical systems that generalize beyond a training dataset? 
• Which generative models are most appropriate for a given task? 
• How do we train transferable models that are appropriate for a large class of systems?
• Which criteria or metrics should we use to evaluate the quality of generative models?