Project for MSE 593 Data-Driven Materials Design and Genomics

Project: 2D materials as memristor candidates using high throughput phase field simulations and machine learning

The proposed research seeks to accelerate the rate of identifying suitable 2D materials for RRAM devices by developing high-throughput phase-field model simulations. Using machine learning techniques (SISSO, Genetic programming, or Unsupervised ML) to interpret the data can aid in understanding the important material parameters that influence resistive switching in the devices. Figure: Combined experiment, machine learning and high-throughput simulations to study the correlation between materials properties and device performance of memristor based on 2D materials.