(411e) Advancing Machine Learning Methodology for the Chemical and Materials Domain
AIChE Annual Meeting
2019
2019 AIChE Annual Meeting
Topical Conference: Applications of Data Science to Molecules and Materials
Innovations in Methods of Data Science
Tuesday, November 12, 2019 - 4:30pm to 4:45pm
The use of modern machine learning, informatics, and data mining approaches is a relatively new development in the chemical and materials domain. These techniques have been exceedingly successful in other application fields, and since there is no fundamental reason why they should not have a similarly transformative impact on chemical and materials research, there is now a concerted effort by the community to introduce data science in this new context. However, adapting techniques from other application domains for the study of chemical and materials systems requires a substantial rethinking and redevelopment of the existing methods.
In this presentation we will discuss our work on designing advanced, physics-infused neural network architectures, the fusion of unsupervised clustering with supervised regression for local ensemble models, active and transfer learning techniques, bootstrapping approaches to minimize our training data footprint, methods to increase the applicability domain of data-derived models, and automated hyperparameter optimization.