Chemical Space Exploration and Molecular Design of Organic Donor–Acceptor TADF Emitters
Journal of Materials Chemistry C · DOI: 10.1039/D6TC00707D
Computational chemistry · scientific software · molecular discovery
Tools, chemical space, and interpretable models for molecular discovery.
AnoopLab is currently based at Digital University Kerala, continuing a computational chemistry research programme developed across Digital University Kerala and IIT Kharagpur.
We develop and apply computational methods to explore molecular structure, reactivity, and materials. Machine learning is used judiciously alongside quantum chemistry, with emphasis on chemical interpretation, validation, and reproducible workflows.
01 · Research programme
Four connected themes
The programme is organised around methods and questions rather than isolated application areas.
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Scientific software and reproducible computational discovery
Reusable tools, documented workflows, and research datasets for chemical structure, reaction, and property exploration. PyAR is the principal software-development programme. Explore the PyAR research theme
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Explainable AI and machine learning for molecular science
Interpretable structure–property relationships and ML-assisted quantum chemistry, with attention to chemical meaning, validation, and the limits of each model.
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Automated exploration of chemical space, reactions, and structures
Systematic generation and exploration of molecules, reaction networks, aggregates, and nanoclusters, including global optimisation and multilevel refinement. Explore reaction discovery Explore aggregates, nanoclusters and nanoalloys
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Computational molecular and materials discovery
Quantum-chemical and data-driven investigations spanning functional molecules, high-energy-density hydrocarbons, nanoclusters, and materials-oriented chemical problems. Explore molecular discovery and functional materials
How this programme emerged. Attempts to explain experimental reactions, structures and properties exposed the limits of one-at-a-time modelling—and led us towards automated discovery, functional molecular design and AI-assisted molecular science.
Read the collaborative research story02 · Scientific evidence
Selected work
Recent developments and representative methodological foundations.
Data-Driven Exploration of Synthesizable Strained Hydrocarbons as High-Energy-Density Candidates for Sustainable Aviation Fuels
Sustainable Energy & Fuels 10, 3240–3251 · DOI: 10.1039/D6SE00364H
Computational Exploration of Small Hydrocarbon Networks: A Stochastic Generation and DFT Refinement Framework
Journal of Computational Chemistry 46, e70236
A Global Optimizer for Nanoclusters
Frontiers in Chemistry 7, 644
Research theme: molecular aggregates, nanoclusters and nanoalloys
Prebiotic Chemistry of HCN Tetramerization by Automated Reaction Search
Chemistry – A European Journal 24, 4885–4894
A Tabu-Search Based Strategy for Modeling Molecular Aggregates and Binary Reactions
Computational and Theoretical Chemistry 1111, 69–81
03 · Software and data
Research infrastructure built in the group
Flagship programme
PyAR
PyAR supports automated exploration of aggregates, reaction pathways, solvation structures, and atomic clusters. Its continued development is central to the group's ambition in software-enabled computational chemistry.
Explore the PyAR research programmeRepository and documentation
HydroMol
A searchable dataset and chemical-space explorer for hydrocarbons.
Explore HydroMolSSA
An implementation of Gillespie's stochastic simulation algorithm.
View the repository04 · People and continuity
Current group
Current researchers are shown here; the complete alumni record is maintained separately.
AnoopLab alumni include former PhD and MSc researchers working across academia, research institutes, and related scientific roles.
Complete alumni record and group photographs05 · Contact
AnoopLab
School of Digital Sciences
Digital University Kerala
Technopark Phase IV, Pallipuram
Thiruvananthapuram, Kerala 695317