Improving deep learning model performance under parametric constraints for materials informatics applications.

ABSTRACT: Modern machine learning (ML) and deep learning (DL) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input representations to output properties for a better understanding of the scientific phenomenon. While a deep neural network comprised of fully connected layers has been widely used for materials property prediction, simply creating a deeper model with a large number of layers often faces with vanishing gradient problem, causing a degradation in the performance, thereby limiting usage. In this paper, we study and propose architectural principles to address the question of improving the performance of model training and inference under fixed parametric con

SUBMITTER: Gupta V 

PROVIDER: S-EPMC10241826 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature