Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/165413
Title: Topological feature engineering for machine learning based halide perovskite materials design
Authors: Anand, D. Vijay
Xu, Qiang
Wee, Junjie
Xia, Kelin
Sum, Tze Chien
Keywords: Science::Physics
Issue Date: 2022
Source: Anand, D. V., Xu, Q., Wee, J., Xia, K. & Sum, T. C. (2022). Topological feature engineering for machine learning based halide perovskite materials design. Npj Computational Materials, 8(1). https://dx.doi.org/10.1038/s41524-022-00883-8
Project: M4081842.110 
RG109/19 
MOE-T2EP50120-0004 
MOE-T2EP20120-0013 
NRF-NRFI-2018-04 
Journal: npj Computational Materials 
Abstract: Accelerated materials development with machine learning (ML) assisted screening and high throughput experimentation for new photovoltaic materials holds the key to addressing our grand energy challenges. Data-driven ML is envisaged as a decisive enabler for new perovskite materials discovery. However, its full potential can be severely curtailed by poorly represented molecular descriptors (or fingerprints). Optimal descriptors are essential for establishing effective mathematical representations of quantitative structure-property relationships. Here we reveal that our persistent functions (PFs) based learning models offer significant accuracy advantages over traditional descriptor based models in organic-inorganic halide perovskite (OIHP) materials design and have similar performance as deep learning models. Our multiscale simplicial complex approach not only provides a more precise representation for OIHP structures and underlying interactions, but also has better transferability to ML models. Our results demonstrate that advanced geometrical and topological invariants are highly efficient feature engineering approaches that can markedly improve the performance of learning models for molecular data analysis. Further, new structure-property relationships can be established between our invariants and bandgaps. We anticipate that our molecular representations and featurization models will transcend the limitations of conventional approaches and lead to breakthroughs in perovskite materials design and discovery.
URI: https://hdl.handle.net/10356/165413
ISSN: 2057-3960
DOI: 10.1038/s41524-022-00883-8
DOI (Related Dataset): 10.21979/N9/CVJWZ9
Schools: School of Physical and Mathematical Sciences 
Rights: © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http:// creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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