Research approach
Computational chemistry research
A. Anoop’s research in quantum chemistry, automated molecular and reaction exploration, scientific machine learning and PyAR software at AnoopLab.
My research combines quantum chemistry, automated chemical exploration and scientific machine learning to discover and understand molecular structures and reactions. With students and collaborators at AnoopLab, I develop scientific software, including PyAR, and apply these methods to molecular aggregates, nanoclusters, reaction discovery and functional molecular design.
Research approach · Selected studies · Using PyAR · Research enquiries
Research approach
A computational result is only as useful as the question and the candidates that produced it. Much of my work began with calculations on a proposed structure, mechanism or molecule. Over time, the more interesting problem became what had not been proposed: alternative products, lower-energy structures and overlooked regions of chemical space.
Exploring before selecting
Hand-selected candidates inevitably carry a researcher’s assumptions. My work on automated chemical exploration therefore focuses on methods that generate structures, navigate reaction pathways and search large spaces more systematically. Automation does not remove chemical judgement; it makes assumptions explicit and allows them to be tested across more possibilities.
Using machine learning scientifically
When the number of candidates outgrows the number that can be examined with demanding calculations, machine learning can accelerate energy estimates, guide which candidates are evaluated next and reveal relationships worth testing. It is useful only when the model’s training domain, uncertainty and failure modes remain visible. Benchmark performance alone is not a scientific conclusion.
Keeping chemistry as the test
Search algorithms and learned models propose and prioritise. Electronic-structure calculations, mechanistic reasoning and comparison with experiment, where available, determine whether the resulting candidates are credible. I am interested in workflows in which each method has a defined role and the outputs remain inspectable and reproducible.
A collective programme
These ideas are developed with students and collaborators through AnoopLab. The group programme applies them to reaction discovery, molecular and nanocluster structures, and functional molecular design.
Explore the AnoopLab research programme →
Read how collaborations shaped it →
Browse the publication archive →
Selected studies
These papers illustrate the methods and applications behind the research programme:
- Automated search methods. S. Nandi, S. R. McAnanama-Brereton, M. P. Waller and A. Anoop, “A tabu-search based strategy for modeling molecular aggregates and binary reactions,” Computational and Theoretical Chemistry 1111, 69–81 (2017).
- Prebiotic reaction discovery. S. Nandi, D. Bhattacharyya and A. Anoop, “Prebiotic Chemistry of HCN Tetramerization by Automated Reaction Search,” Chemistry – A European Journal 24, 4885–4894 (2018).
- Search with neural-network potentials. S. Giri and A. Anoop, “Exploring the Chemical Space of Noncovalent Molecular Clusters Using Automated Cluster Building Algorithm and Neural Network Potential,” Journal of Computational Chemistry 46, e70287 (2025).
The full publication archive covers the broader programme. These examples describe computational methods and applications; candidate structures and reaction pathways require further chemical validation.
What is PyAR?
PyAR (Python program for aggregation and reaction) is open-source scientific software developed within AnoopLab for generating and exploring candidate molecular structures and reactions. The PyAR research overview explains the scientific approach and links to the supporting papers.
For a software issue, include the command you ran, the PyAR version and the error output, with a small example where possible. Remove any private data before posting to the public issue tracker.
Research enquiries
Prospective students
If you are interested in research with me, begin with the AnoopLab research programme and recent publications. These give a sense of the questions and methods our group works on.
To start a conversation, email me with a brief introduction to your academic background and the research questions that interest you. You may include a CV or a relevant project. For formal admission announcements and procedures, consult Digital University Kerala.
Potential collaborators
Our work connects automated structure and reaction searches, quantum chemistry and scientific machine learning. The collaboration overview shows how these methods connect with other research areas.
For a possible collaboration, email me with the chemical question you want to investigate and the experimental data, computational methods or expertise you would bring to it.