A shared-weight neural network architecture for predicting molecular properties.

Quantum chemical methods scale poorly with increasing molecular size and machine learning models have emerged as a promising, computationally-efficient alternative. We present a shared-weight neural network architecture based on modified atom-centered symmetry functions (ACSFs) and show that it performs similarly to the more computationally expensive per-element neural networks of previous work with ACSFs. The model achieves chemically accurate predictions, with a mean absolute error as low as 0.

Electrophoretic control of concentration polarization in membrane plasmapheresis.

Membrane plasmapheresis is the separation of plasma from whole blood using microfiltration membranes. A major operating problem of membrane plasmapheresis is concentration polarization, the buildup of retained cells or solutes on the membrane surface. In this study, an electric field was used to control concentration polarization by transporting the negatively charged cells and solutes away from the membrane surface.