Machine Learning-Assisted Design and Discovery of Next Generation 2D Materials

Atomically thin two-dimensional materials have attracted interest in the fields of electrochemistry, catalysis, and photonics due to the ease with which their properties may be tuned. First-principles calculations have proven an essential tool in the quest for new 2D materials with tailored properties. However, an exhaustive exploration of the parameter space is infeasible even in the monolayer case. In this work, we use a novel machine learning technique – Crystal Graph Convolutional Neural Networks (CGCNN) [1] – to train accurate models that can predict monolayer 2D material properties more efficiently than density functional theory simulations. Previously, CGCNN architectures have been demonstrated to successfully predict properties of solid electrolytes [2]. Here, we leverage their power to find design principles for 2D materials in light-absorbing and water-splitting applications.

[1] T. Xie, J. C. Grossman, Phys. Rev. Lett. 120, 145301 (2018).
[2] Z. Ahmad, T. Xie, C. Maheshwari, J. C. Grossman, and V. Viswanathan, ACS Cent. Sci. 4, 996 (2018).