Chemical Space Exploration and Molecular Design of Organic Donor–Acceptor TADF Emitters
Journal of Materials Chemistry C · DOI: 10.1039/D6TC00707D
Computational chemistry · automated exploration · scientific software
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 computational methods and research software for automated exploration of chemical structure, reaction and molecular space. Quantum chemistry, search algorithms and scientific machine learning are combined to build reproducible workflows for molecular discovery, with emphasis on chemical interpretation and rigorous validation.
01 · Research programme
A connected methodological programme
Automated exploration asks how chemical possibilities can be generated and searched systematically. Scientific machine learning helps make those searches faster, more selective and more interpretable when the scale of the problem requires it.
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Automated chemical exploration
Methods and software for generating molecular structures, navigating reactions and exploring chemical space beyond hand-selected candidates. Research approach PyAR
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Scientific machine learning
Chemically grounded models that accelerate calculations, guide search and reveal interpretable structure–property relationships, with explicit attention to validation, uncertainty and domain limits. Research approach
Reaction discovery, structural exploration and functional molecular design provide the main application areas in which the two directions meet.
Explore the full research programme02 · 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 is our flagship research-software programme for automated exploration of molecular structures, reactions, aggregates and atomic clusters. Current development focuses on making these workflows more robust, extensible and usable beyond our own group.
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