# Computational discovery of new 2D materials using deep learning generative models

arXiv: https://arxiv.org/abs/2012.09314 · HTML: https://mlsanity.com/paper/2012.09314/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [Deep Reinforcement Learning for Inverse Inorganic Materials Design](https://mlsanity.com/paper/2210.11931/) (2022)
- [Data-driven discovery of novel 2D materials by deep generative models](https://mlsanity.com/paper/2206.12159/) (2022)
- [MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art](https://mlsanity.com/paper/2109.04007/) (2021)
- [Physics guided deep learning generative models for crystal materials discovery](https://mlsanity.com/paper/2112.03528/) (2021)
- [Semi-supervised teacher-student deep neural network for materials discovery](https://mlsanity.com/paper/2112.06142/) (2021)
- [Global optimization in the discrete and variable-dimension conformational space: The case of crystal with the strongest atomic cohesion](https://mlsanity.com/paper/2302.13537/) (2023)
- [Towards Imbalanced Large Scale Multi-label Classification with Partially Annotated Labels](https://mlsanity.com/paper/2308.00166/) (2023)
- [Rapid Discovery of Graphene Nanocrystals Using DFT and Bayesian Optimization with Neural Network Kernel](https://mlsanity.com/paper/2208.07612/) (2022)
- [AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning](https://mlsanity.com/paper/2404.04810/) (2024)
- [Revising the Problem of Partial Labels from the Perspective of CNNs' Robustness](https://mlsanity.com/paper/2407.17630/) (2024)

## Abstract (arXiv)

Two dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties. While several thousand 2D materials have been screened in existing materials databases, discovering new 2D materials remains to be challenging. Herein we propose a deep learning generative model for composition generation combined with random forest based 2D materials classifier to discover new hypothetical 2D materials. Furthermore, a template based element substitution structure prediction approach is developed to predict the crystal structures of a subset of the newly predicted hypothetical formulas, which allows us to confirm their structure stability using DFT calculations. So far, we have discovered 267,489 new potential 2D materials compositions and confirmed twelve 2D/layered materials by DFT formation energy calculation. Our results show that generative machine learning models provide an effective way to explore the vast chemical design space for new 2D materials discovery.