Hundreds of new, stable, one-dimensional materials from a generative machine learning model

Abstract

We use a generative neural network model to create thousands of new, one-dimensional materials. The model is trained using 508 stable one-dimensional materials from the Computational 1D Materials Database (C1DB) database. More than 500 of the new materials are shown with density functional theory calculations to be dynamically stable and with heats of formation within 0.2 eV of the convex hull of known materials. Some of the new materials could also have been obtained by chemical element substitution in the training materials, but completely new classes of materials are also produced. The band structures, electronic densities of states, work functions, effective masses, and phonon spectra of the new materials are calculated, and the data are added to C1DB.

Machine-Learning-Driven Expansion of the 1D van der Waals Materials Space

“…We note that our effort is complementary to recent ML-based investigations which aim at expanding 1D material space 18,19 by training over crystal structures. We take a different approach of omitting the middle step of predicting structures and instead utilizing density functional theory (DFT) as a postverification process.…”