This workshop aims to create novel synergistic collaborations between researchers in two different fields: modeling of many-particle (quantum and classical) systems and machine learning. Interactions between many constituent particles generally give rise to collective (or emergent) phenomena in matter. Even when the interactions between the particles are well defined and the governing equations of the system are understood, the collective behavior of the system as a whole does not trivially emerge from these equations.
Machine learning (ML) methods have been used extensively in a wide variety of fields ranging from e.g. the neurosciences, genetics, multimedia search to drug discovery. Recently, ML techniques have started to be vigorously applied for understanding many-particle systems. However, this is an emergent field and many open questions remain. Therefore, the aim of this IPAM workshop is to shine light into the ML “black box” by bringing together experts in many-particle systems in condensed-matter physics, materials, chemistry, and protein folding, together with experts in machine learning to synergetically address the problem of tackling emergent behavior and understanding the underlying collective variables in these systems.
This workshop will address the reaches and limitations of ML as applied to many-particle systems and highlight examples where physical models can be successfully combined with ML algorithms. It will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.