Topological feature engineering for machine learning based halide perovskite materials design
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. Howeve...
Saved in:
Main Authors: | Anand, D. Vijay, Xu, Qiang, Wee, Junjie, Xia, Kelin, Sum, Tze Chien |
---|---|
Other Authors: | School of Physical and Mathematical Sciences |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/165413 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Feature issue introduction : halide perovskites for optoelectronics
by: White, Thomas P., et al.
Published: (2019) -
High-Quality Whispering-Gallery-Mode Lasing from Cesium Lead Halide Perovskite Nanoplatelets
by: Zhang, Qing, et al.
Published: (2017) -
Slow hot-carrier cooling in halide perovskites : prospects for hot-carrier solar cells
by: Li, Mingjie, et al.
Published: (2020) -
Halide perovskite solar cells
by: Li, Zhe
Published: (2024) -
Compositional and morphological changes in water-induced early-stage degradation in lead halide perovskites
by: Chen, Shi, et al.
Published: (2020)