Trainable Molecular Dynamics Models

The development of automatic differentiation is motivated by the desire to train computational models with non-trivial architectures that more accurately reflect the underlying structure of the data. This is especially desirable when studying complex physical phenomena, which are governed by fundamental principles (e.g. conservation of energy) that are well understood. By applying automatic differentiation to well-established physics simulations, one can in principle obtain machine trainable models with the physics built in. We will discuss the first steps towards Trainable Molecular Dynamics Models (TMDMs): how they work, their significant potential for scientific and technological discovery, and initial discoveries of non-trivial self-assembly pathways.