Excited-state photophysics
Understand the mechanism
Resolve how molecular structure and conformation govern emission, electron transfer, and non-radiative decay.
DFT / TD-DFT · CASSCF / CASPT2 · Nonadiabatic dynamics
Computational chemistry & AI for materials
Syed Ali Abbas
Abedi.
I study what molecules do after they absorb light—and how to use that knowledge to design better ones.
I connect excited-state quantum chemistry with the design of fluorophores and photofunctional materials. Now, as a Research Fellow at NUS I-FIM, I work on delta machine learning within the NRF Materials Data Foundry project.
Research in numbers
Google Scholar ↗Snapshot · 09 Sep 2026 608Citations
14h-index
16i10-index
Research approach
From mechanism
to molecular function.
Fluorophores & photostability
Design for function
Translate photophysical mechanisms into design principles for imaging probes, photoswitches, and photoactive materials.
PET · TICT · ESIPT · Structure–property relationships
Current work · Materials Data Foundry
Learn across fidelities
Work on delta machine learning for materials discovery, connecting predictions across different levels of theory.
Delta learning · Multi-fidelity ML · Python · HPC
Selected contributions
The work, in print.
A general strategy towards multicolour fluorogenic peptides for wash-free bioassays ↗
Molecular design for multicolour fluorescence in biological assays.
Mechanistic insights into the design of fluorogenic molecules for wash-free biological applications ↗
Connecting fluorogenic mechanisms with the design of wash-free probes.
Super-photostable organic dye for long-term live-cell single-protein imaging ↗
Photostability in the context of sustained single-protein observation.
Restriction of twisted intramolecular charge transfer enables aggregation-induced emission ↗
First-author mechanistic study of dialkylamino-naphthalene derivatives.
Current work · NRF Materials Data Foundry
Learning the difference.
Accelerating discovery.
How can lower-cost calculations help us predict materials properties at a higher level of accuracy?
At NUS I-FIM, I work on delta machine learning within the Materials Data Foundry project. Delta learning models the difference between levels of theory, building on a lower-cost prediction to approach a more accurate reference.
Explore the current direction →Recent updates
From the research desk.
Multicolour fluorogenic peptides in Nature Chemistry
Our collaborative work, “A general strategy towards multicolour fluorogenic peptides for wash-free bioassays”, was published online in Nature Chemistry.
Research Fellow at NUS I-FIM
I joined NUS I-FIM as a Research Fellow, working on delta machine learning within the NRF Materials Data Foundry project. About the Materials Data Foundry.
FB3 2026 in Torino: presented and connected
Shared work and met colleagues at the 6th International Conference on Fluorescent Biomolecules and their Building Blocks in Italy, with support from NTU and MOE Singapore.
Connect & collaborate
A molecular question
worth working on?
I welcome conversations about excited-state mechanisms, fluorophore design, photostability, and AI for materials discovery.
abedisyedaliabbas@gmail.com ↗