| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
ICME Gap Analysis in Materials Informatics: Databases, Machine Learning, and Data-Driven Design
|
| Presentation Title |
L-20: Data Driven Prediction of Crystallographic Attributes of Small Molecules Using Various Molecular Fingerprints |
| Author(s) |
Piyush Karande, Peggy Li, Soo Kim, Joanne Kim, Hyojin Kim, Donald Loveland, T. Yong-Jin Han |
| On-Site Speaker (Planned) |
Piyush Karande |
| Abstract Scope |
Crystal structures of organic molecules dictate several key properties that are critical in various industrial applications. Conventional methods to predict crystal structure rely heavily on expensive physics based computational models and simulations. As a potential alternative, here we propose a data driven approach to predict several different crystallographic attributes such as density, symmetry, and crystal packing motifs. We use the 3D structure of a subset of molecules from the Cambridge Crystal Structure Database and investigate the feasibility of various molecular fingerprinting and machine learning methods to predict the attributes. We present results using a) 3D Convolutional network, b) Graph convolutional networks, and c) Extended 3D Fingerprint with support vector machines and random forests, to predict these quantities. Each of these methods produce promising results and provides an insight into the information embedded in the 3D structure of these organic molecules. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |