|About this Abstract
||MS&T21: Materials Science & Technology
||Accelerating Materials Science with Big Data and Machine Learning
||Machine Learning in 2D Materials: Benchmarking Crystal Graph Based Convolutional Neural Network (CGCNN) for Open Databases
||Shreeja Das, Raj Kishore, Mihir Ranjan Sahoo, S Swayamjyoti, Anthony Yoshimura, Nikhil Koratkar, Saroj Kumar Nayak, Kisor Kumar Sahu
|On-Site Speaker (Planned)
Identifying the right 2D materials for a targeted application is a non-trivial task because of a huge number of combinatorial possibilities. Unlike 3D bulk materials, the unavailability of large databases for 2D materials poses a unique challenge for most machine learning protocols. We employ the recently developed crystal graph convolutional neural network to benchmark some of the open databases of 2D materials for predicting both theoretical and experimental properties of 2D materials. The results indicate, bulk materials trained models are non-transferable for predicting formation energies of 2D materials. Even with a much smaller training size, 2D materials data trained models were able to capture the local chemical environment and energetics of different configurations of metal doped 2D MoS2. We also benchmark the databases in their ability to help in predicting experimental bandgaps of 18 different 2D materials. Models trained on PBE bandgaps severely underpredict optical bandgaps of materials.