Physics-based extraction of material parameters from perovskite experiments <i>via</i> Bayesian optimization-Reference-Cited by-同舟云学术

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Physics-based extraction of material parameters from perovskite experiments via Bayesian optimization

Published:2024 Issue:13 Volume:17 Page:4735-4745
ISSN:1754-5692
Container-title:Energy & Environmental Science
language:en
Short-container-title:Energy Environ. Sci.

Author:

Zhan Hualin¹^ORCID,Ahmad Viqar¹^ORCID,Mayon Azul¹,Dansoa Tabi Grace¹^ORCID,Bui Anh Dinh¹,Li Zhuofeng¹,Walter Daniel¹^ORCID,Nguyen Hieu¹^ORCID,Weber Klaus¹^ORCID,White Thomas¹^ORCID,Catchpole Kylie¹

Affiliation:

1. School of Engineering, Australian National University, ACT 2601, Australia

Abstract

We present a materials analysis platform that extracts fundamental material parameters from experimental data via interpretable machine learning. This platform is adaptable to other materials/experiments and is implementable on desktop computers.

Funder

Australian Renewable Energy Agency

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/EE/D4EE00911H

Reference35 articles.

1. Charge Carriers in Planar and Meso-Structured Organic–Inorganic Perovskites: Mobilities, Lifetimes, and Concentrations of Trap States

2. Electron-Hole Diffusion Lengths Exceeding 1 Micrometer in an Organometal Trihalide Perovskite Absorber

3. Charge-Carrier Mobilities in Metal Halide Perovskites: Fundamental Mechanisms and Limits

4. A unified mobility model for device simulation—I. Model equations and concentration dependence

5. Resistivity, mobility and impurity levels in GaAs, Ge, and Si at 300°K

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