  Recent development in computer processing power leads to new paradigms of how
problems in many-body physics and especially polymer physics can be addressed.
GPU parallel processors can be employed to generate millions of independent
configurations of polymeric molecules of heterogeneous sequence in complex
environments at a second, and concomitant free-energy landscapes estimated.
Resulting data bases that are complete in terms of polymer sequence and
architecture are a powerful training basis for multi-layer artificial neural
networks, whose internal representations will potentially lead to a new
physical viewpoint in how sequence patterns are linked to effective polymer
properties and response to the environment. In our example, we consider the
translocation time of a copolymer through an amphiphilic bilayer membranes as a
function of binary sequence of hydrophilic and hydrophobic units. First we
demonstrate that massively parallel Rosenbluth sampling for all possible
sequences of a polymer allows for meaningful dynamic interpretation in terms of
the mean first escape times through the membrane. Second we train a multi-layer
perceptron, and show by a systematic reduction of the training set to a narrow
window of translocation times, that the neural network develops internal
representations of the physical rules mapping sequence to translocation times.
In particular, based on the narrow training set, the network predicts the
correct order of magnitude of translocation times in a window that is more than
8 orders of magnitude wider than the training window.
