UPDATE: This talk will be rescheduled to a new date after the summer.

You are all cordially invited to the AMLab seminar on Tuesday **June 12** at 16:00 in C3.163, where **Bela Mulder** (AMOLF) will give a talk titled “**Pitting man against machine in the arena of bottom-up design of crystal structures**”. Afterwards there are the usual drinks and snacks!

**Abstract**: In this highly informal seminar I would like to pitch the question “Can a machine learning system develop a theory?” One of the much-touted properties of deep learning networks is that their deeper levels develop higher order generalization representations of their inputs. This begs the question whether they are able to hit upon the type of hidden structures in physical problem that are the cornerstone of effective physical theories. I would like to propose to test this idea in a concrete setting related to the highly relevant question of inverse design of self-assembling matter. I have recently formulated a novel approach towards inferring the specific short range isotropic interactions between particles of multiple types on lattices of given geometry in order that they spontaneously form specified periodic states of essentially arbitrary complexity. This approach rests upon the subtle intertwining between the group of transformations that leave the lattice structure invariant, with the group of permutations in the set of particle types induced by these same transformations on the target ordered structure. The upshot of this approach is that the number of independent coupling constants in the lattice can be systematically reduced from O(N^{2}), where N is the number of distinct species, to O(N). The idea would be to see whether a machine learning approach which uses the space of possible patterns and their trivial transforms under symmetry operations as input, the set of possible constants as outputs, and feedback based on the degree to which the target structure is realized with these coupling constants is able to “learn” the symmetry-based rules, in a way that also generalizes to similar patterns not included in the training set.