Issue 3, 2018

Classification of spatially resolved molecular fingerprints for machine learning applications and development of a codebase for their implementation

Abstract

Direct mapping between material structures and properties for various classes of materials is often the ultimate goal of materials researchers. Recent progress in the field of machine learning has created a unique path to develop such mappings based on empirical data. This new opportunity warranted the need for the development of advanced structural representations suitable for use with current machine learning algorithms. A number of such representations termed “molecular fingerprints” or descriptors have been proposed over the years for this purpose. In this paper, we introduce a classification framework to better explain and interpret existing fingerprinting schemes in the literature, with a focus on those with spatial resolution. We then present the implementation of SEING, a new codebase to computing those fingerprints, and we demonstrate its capabilities by building k-nearest neighbor (k-NN) models for force prediction that achieve a generalization accuracy of 0.1 meV Å−1 and an R2 score as high as 0.99 at testing. Our results indicate that simple and generally overlooked k-NN models could be very promising compared to approaches such as neural networks, Gaussian processes, and support vector machines, which are more commonly used for machine learning-based predictions in computational materials science.

Graphical abstract: Classification of spatially resolved molecular fingerprints for machine learning applications and development of a codebase for their implementation

Article information

Article type
Paper
Submitted
09 Qun 2018
Accepted
19 Nah 2018
First published
20 Nah 2018

Mol. Syst. Des. Eng., 2018,3, 431-441

Classification of spatially resolved molecular fingerprints for machine learning applications and development of a codebase for their implementation

M. Reveil and P. Clancy, Mol. Syst. Des. Eng., 2018, 3, 431 DOI: 10.1039/C8ME00003D

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements