White Paper: Understanding Many-Particle Systems with Machine Learning - IPAM

This white paper was prepared by the participants of the fall 2016 long program Understanding Many-Particle Systems with Machine Learning. Interactions between many constituent particles, i.e. quarks, electrons, atoms, molecules, or materials, 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. Despite many decades of prominent work on interacting many-particle (MP) systems, the problem of N interacting particles is not exactly soluble. In fact, computational complexity typically increases exponentially with N.