Welcome to the nexSAS (next-generation Science-AI Synergy) group at The Australian National University! In nexSAS, we use AI to revolutionize solar and energy storage technology by uncovering new physics and developing new research techniques.
News
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November 07, 2025Welcome aboard, Xin! We are thrilled to have you join us.
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October 28, 2025
Funded by ARC: Mechanistic analysis of perovskite degradation for stable photovoltaics
Well done, nexSAS!!! Let’s transform PV technology using our physics-based AI!
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October 14, 2025
Published a Book Chapter: Machine learning for predicting and improving perovskite properties
This chapter surveys how machine learning is transforming perovskite research by addressing challenges in synthesis, characterization, and device optimization. It contributes clear workflows and case studies demonstrating structure–property mapping with reduced cost, enabling high-throughput screening, multi-scale prediction, composition–process optimization, and automated fabrication. It also highlights key challenges—data scarcity, heterogeneity, and interpretability—and outlines emerging opportunities in intelligent automation for next-generation optoelectronics.
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July 21, 2025
Published in Advanced Functional Materials: Oxygen-containing groups as a key to enable high-efficiency (certificed 26.35%) and high-stability (≥1100 hours) PV cells
Congratulations, Yiqing, on your outstanding achievment as a first-year PhD student! Perovskites like oxygen, so we can feed them before they have the chance to capture the free-state oxygen and become unstable. By providing stable, compound-state oxygen to the uncoordinated Pb and FA molecules and forming strong bonds such as Pb-O, we have achieved high-efficiency (certified 26.35%) and high-stability (T95≥1100h under one-sun continuous illumination, 40 ℃, RH 35%, Vmpp tracking) perovskite solar cells.
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July 11, 2025
Published in JMCA: Molecular locking of defects enables high-efficiency (certificed 26%) and high-stability (≥1200 hours) PV cells
Congratulations, Yiqing, on your first paper at ANU as a first-year PhD student! The C=O and S=O groups in 4-methylsulfonylbenzoic acid (4-MeSBA) provide strong binding with the perovskite grains, enabling high-efficiency (certified 26%) and high-stability (T92≥1200h under one-sun continuous illumination, 40 ℃, RH 35-45%, Vmpp tracking) perovskite solar cells.
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July 01, 2025
Published in Advanced Energy Materials: Multi-site coordination of perovskite surfaces by additives for enhanced crystallization and reduced defects
With multiple S=O groups, a signle molecule called OBSC can strongly bind to the Pb atoms at two perovskite grain surfaces, holding different grains together, enhancing crystallization, and reducing defects. These effects contribute to high-efficiency (certified 26.03%) and high-stability (T96≥1100h under one-sun continuous illumination) perovskite solar cells.
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June 18, 2025
Published in Advanced Materials: Dual-site passivation by sulfonamide for high-efficiency (certificed 26.31%) and high-stability (≥1100 hours) PV cells
At the atomistic level, we uncovered a dual-site passivation mechanism facilitated by sulfonamide molecules. These molecules effectively coordinate with undercoordinated atoms (dangling bonds) on the perovskite surface, thereby reducing defect densities and promoting improved crystallization. Together, these effects contribute to the realization of high-efficiency (certificed 26.31%), high-stability (T96≥1100h) perovskite solar cells.
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June 01, 2025Welcome, Yinbao! Thank you for joining us.
Research
Mechanistic analysis of perovskite degradation for stable photovoltaics
Physics-based AI for mechanistic analysis of perovskite degradation
Physics-based AI for Robots
Empowering the robotic high-throughput platform to think like a scientist.
Atomic origin of instability at the perovskite interfaces
AI-enhanced atomistic study of interface physics.
Transport phenomenon at the tri-phase interface in hydrogen energy systems
This research investigates the transport of electrons, ions, and molecules at the tri-phase interface in hydrogen energy systems.
Development of a low-cost and quantitative materials analysis platform by physics-based machine learning
ACAP Postdoctoral Fellowship (R4, 2023).
Development of user friendly machine learning toolkit for perovskite solar cell analysis
An Honours project for Bachelor of Computer Science
People
Group Leader
Dr Hualin Zhan
Theoretical physicist with expertise in machine learning since 2006.
Graduate Students
Yiqing Zhang
PhD student. Chemist with expertise in computational materials.
Yinbao Li
PhD student. AI algorithm engineer.
Xin Wang
PhD student. Computer scientist.
Collaborators
Prof Kylie Catchpole
FAA, FTSE, ANU
Prof Klaus Weber
ANU
Prof Udo Bach
Monash University
Prof Zhe Liu
University of Melbourne
Prof Jun Peng
Soochow University
Prof Lianhai Zu
Chinese Academy of Sciences
Alumni
Jifeng Li
Bachelor of Computer Science (ANU), 2022
Funding
We sincerely acknowledge the generous financial support from the following funding agencies.
