First-principles simulation of point defects for neuromorphic devices

As conventional materials such as silicon are approaching quantum mechanical limits, a new paradigm beyond Moore’s law is needed. One promising solution is neuromorphic computing, which aims to develop devices based on our own brain capable of learning and making nuanced decisions. Neurons operate with up to a 1000 times smaller power dissipation compared to devices based on conventional semiconductors, and thus offer a sustainable model for the next-generation of computing infrastructure. We seek to utilize the defect chemistry of low-dimensional and perovksite materials to understand the atomic processes that underlie resistive switching. Students will expect to learn basics of high-performance computing, programming, and solid-state chemistry/physics.

The Wang Materials Group is an interdisciplinary research group at the intersection of computation and materials science. Our group utilizes and deploys computational methods to engineer the optical, electronic, and transport properties of materials in energy sustainability technologies. Enabled by high performance computing (HPC), we seek to elucidate and predict the materials properties at the microscopic level using first-principles calculations, drive the exploration of novel materials platforms, and create strategies that directly couple to/guide experiments. In particular, we look to understand and harness defects in materials for optoelectronic devices. As a group, we value diversity, equity, and inclusion and strive to foster each.