Accelerated discovery of high-performance small-molecule hole transport materials via molecular splicing, high-throughput screening, and machine learning DOI Open Access

Jiansen Wen,

Shu-Wen Yang, Linqin Jiang

et al.

Journal of Materials Informatics, Journal Year: 2025, Volume and Issue: 5(3)

Published: April 15, 2025

As the most representative and widely utilized hole transport material (HTM), spiro-OMeTAD encounters challenges including limited mobility, high production costs, demanding synthesis conditions. These issues have a notable impact on overall performance of perovskite solar cells (PSCs) based hinder its large-scale commercial application. Consequently, there exists strong demand for high-throughput computational design novel small-molecule HTMs (SM-HTMs) that are cost-effective, easy to synthesize, offer excellent performance. In this study, systematic iterative development process SM-HTMs is proposed, aiming accelerate discovery application high-performance SM-HTMs. A custom-developed molecular splicing algorithm (MSA) generated sample space 200,000 intermediate molecules, culminating in creation comprehensive database over 7,000 potential SM-HTM candidates. total, six promising HTM candidates were identified through MSA, density functional theory calculations screening. Furthermore, three machine learning algorithms, namely random forest, gradient boosting decision tree, extreme (XGBoost), employed construct predictive models key properties, recombination energy, solvation free maximum absorption wavelength, hydrophobicity. Among these, XGBoost-based model demonstrated best The MSA methodology combining prediction models, as introduced offers powerful universal toolkit optimization next-generation SM-HTMs, thereby paving way future advancements PSCs.

Language: Английский

Advanced TiO2-Based Photocatalytic Systems for Water Splitting: Comprehensive Review from Fundamentals to Manufacturing DOI Creative Commons

Tarek Ahasan,

E.M.N. Thiloka Edirisooriya, Punhasa S. Senanayake

et al.

Molecules, Journal Year: 2025, Volume and Issue: 30(5), P. 1127 - 1127

Published: Feb. 28, 2025

The global imperative for clean energy solutions has positioned photocatalytic water splitting as a promising pathway sustainable hydrogen production. This review comprehensively analyzes recent advances in TiO2-based systems, focusing on materials engineering, source effects, and scale-up strategies. We recognize the advancements nanoscale architectural design, engineered heterojunction of catalysts, cocatalyst integration, which have significantly enhanced efficiency. Particular emphasis is placed crucial role chemistry system performance, analyzing how different sources-from wastewater to seawater-impact evolution rates stability. Additionally, addresses key challenges scaling up these including optimization reactor light distribution, mass transfer. Recent developments artificial intelligence-driven discovery process are discussed, along with emerging opportunities bio-hybrid systems CO2 reduction coupling. Through critical analysis, we identify fundamental propose strategic research directions advancing technology toward practical implementation. work will provide comprehensive framework exploring advanced composite developing efficient scalable multifunctional simultaneous

Language: Английский

Citations

0
Ferroelectric control of valleytronic nonvolatile storage in HfCl2/Sc2CO2 heterostructure DOI

Zhou Cui,

Xunkai Duan,

Jiansen Wen

et al.

Applied Physics Letters, Journal Year: 2025, Volume and Issue: 126(12)

Published: March 1, 2025

Valleytronics, utilizing the valley degree of freedom in electrons, has potential for advancing next-generation nonvolatile storage. However, practical implementation remains challenging due to limited control over valleytronic properties. Here, we propose ferroelectric HfCl2/Sc2CO2 van der Waals heterostructure as a platform overcome these limitations, enabling tunable and behaviors. Our findings show that electric polarization state Sc2CO2 monolayer governs electronic properties heterostructures. Positive induces direct gap at valleys, functionality excitation readout via circularly polarized light, while negative results an indirect-gap, suppressing behavior. Moreover, our transport simulations further demonstrate polarization-dependent p-i-n junction with 8 nm possesses maximum tunnel electroresistance (TER) ratio 1.60 × 108% bias 0.5 eV. These provide insights into ferroelectric-controlled transitions position promising candidate energy-efficient memory storage applications.

Language: Английский

Citations

0
Accelerated discovery of high-performance small-molecule hole transport materials via molecular splicing, high-throughput screening, and machine learning DOI Open Access

Jiansen Wen,

Shu-Wen Yang, Linqin Jiang

et al.

Journal of Materials Informatics, Journal Year: 2025, Volume and Issue: 5(3)

Published: April 15, 2025

As the most representative and widely utilized hole transport material (HTM), spiro-OMeTAD encounters challenges including limited mobility, high production costs, demanding synthesis conditions. These issues have a notable impact on overall performance of perovskite solar cells (PSCs) based hinder its large-scale commercial application. Consequently, there exists strong demand for high-throughput computational design novel small-molecule HTMs (SM-HTMs) that are cost-effective, easy to synthesize, offer excellent performance. In this study, systematic iterative development process SM-HTMs is proposed, aiming accelerate discovery application high-performance SM-HTMs. A custom-developed molecular splicing algorithm (MSA) generated sample space 200,000 intermediate molecules, culminating in creation comprehensive database over 7,000 potential SM-HTM candidates. total, six promising HTM candidates were identified through MSA, density functional theory calculations screening. Furthermore, three machine learning algorithms, namely random forest, gradient boosting decision tree, extreme (XGBoost), employed construct predictive models key properties, recombination energy, solvation free maximum absorption wavelength, hydrophobicity. Among these, XGBoost-based model demonstrated best The MSA methodology combining prediction models, as introduced offers powerful universal toolkit optimization next-generation SM-HTMs, thereby paving way future advancements PSCs.

Language: Английский

Citations

0