Raghavendra Meena, PhD

Postdoctoral researcher and computational materials scientist with expertise in DFT, AIMD, machine-learning interatomic potentials, microkinetic modelling, and LLM-driven workflow automation for sustainable catalysis and materials design.

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Education

2020 – 2025

Ph.D., Computational Heterogeneous Catalysis

Wageningen University & Research, the Netherlands

Advisors: Dr. Guanna Li, Prof. Harry Bitter, Prof. Han Zuilhof

Thesis: Multiscale Modeling of Molybdenum and Tungsten Carbide Catalysts for Sustainable Biomass Conversion

2019 – 2020

M2, Computational Materials Science

Sorbonne Université, Paris, France

Advisors: Prof. Michele Casula, Prof. Prasenjit Ghosh

Thesis: Magnetic properties of narrow zig-zag graphene nanoribbons from ab initio calculations

2015 – 2020

BS-MS, Chemistry and Physics

Indian Institute of Science Education and Research (IISER) Pune, India

Research Experience

Oct 2025 – June 2026

Postdoctoral Researcher

Biobased Chemistry & Technology group, Wageningen University & Research

  • Trained and validated machine-learning interatomic potentials (MACE, Meta's UMA/fair-chem) for CO2 conversion and polymer-degradation chemistry, generating DFT reference data (VASP, CP2K) for training.
  • Built agent-materials-science, an open-source automation agent exposing ASE and DFT tooling through a scripted interface to screen adsorption sites over catalytic surfaces in under a minute per structure — direct experience designing automated, high-throughput evaluation pipelines.
Oct 2020 – Sep 2025

Doctoral Researcher

Biobased Chemistry & Technology group, Wageningen University & Research

  • Systematically screened surface reaction mechanisms, adsorption energies, and transition-state geometries (NEB) across composition, dopant, and surface-termination spaces on transition-metal carbide surfaces using plane-wave DFT (VASP, CP2K).
  • Derived interpretable activity descriptors via SISSO symbolic regression and SHAP explainable AI across large DFT datasets, identifying d-band centre and surface oxygen affinity as key statistical predictors of catalytic activity, then used them to propose materials-improvement strategies.
  • Modelled reaction free-energy landscapes and transition states at catalyst interfaces via ab initio molecular dynamics and metadynamics (CP2K + PLUMED); applied microkinetic modelling to connect energetics to observed activity.
  • Designed and maintained modular, reproducible HPC workflow pipelines (Python/Bash on SLURM, ASE, pymatgen) automating structure generation, DFT input preparation, job submission, and post-processing across thousands of calculations.
  • Worked cross-institutionally (WUR, UU, UvA, TU/e) translating computational screening results into experimental catalyst design; co-authored 10 peer-reviewed publications and contributed to grant applications and peer review.
May 2019 – May 2020

Master's Thesis Researcher

Theory of Quantum Materials group, Sorbonne Université

  • Computed ground-state electronic and magnetic properties of the narrowest zigzag graphene nanoribbon using Quantum ESPRESSO for plane-wave DFT and TurboRVB for quantum Monte Carlo (QMC), establishing many-body QMC reference data for a strongly correlated, low-dimensional carbon system.
  • Benchmarked a hierarchy of DFT exchange-correlation approximations, including DFT+U, against the QMC reference to assess their accuracy for the antiferromagnetic ground state and spin-resolved electronic structure.
  • Developed Python and Bash workflows for plane-wave DFT input generation, k-point and cutoff convergence, and structural relaxation on HPC clusters, directly relevant to building DFT reference datasets for downstream MLIP training.

Publications

Ten peer-reviewed publications in computational chemistry, catalysis, and materials science. Full list on Google Scholar. One first-author manuscript and two collaboration manuscripts in preparation.

First-author publications

  1. Meena, R. Understanding the Dynamics of C–OH Bond Activation over Mo2C under Hydrodeoxygenation Reaction Conditions. Manuscript submitted to RSC Chemical Science.
  2. Meena, R.; Purcell, J. M.; Kluijtmans, W.; Zuilhof, H.; Bitter, J. H.; Ouyang, R.; Li, G. (2026). Activity descriptors of Mo2C-based catalysts for C–OH bond activation. ChemRxiv (manuscript submitted to ACS JPCC). doi:10.26434/chemrxiv-2025-pg52l/v2
  3. Meena, R.; Draijer, K. M.; van Dam, B.; Zuilhof, H.; Bitter, J. H.; Li, G. (2025). Rationalizing catalytic performances of Mo/W-(oxy)carbides for hydrodeoxygenation reaction. ChemCatChem. Front cover. doi:10.1002/cctc.202500659
  4. Meena, R.; Bitter, J. H.; Zuilhof, H.; Li, G. (2023). Toward the rational design of more efficient Mo2C catalysts for hydrodeoxygenation. ACS Catalysis. doi:10.1021/acscatal.3c03728
  5. Meena, R.; Li, G.; Casula, M. (2022). Ground-state properties of the narrowest zigzag graphene nanoribbon from quantum Monte Carlo. Journal of Chemical Physics. doi:10.1063/5.0078234

Co-authored publications

  1. Hergesell, A. H.; Popp, S.; Meena, R.; Guarin, V. M. O.; Seitzinger, C. L.; Sievers, C.; Li, G.; Vollmer, I. (2025). Homolytic fracture of inorganic crystalline materials enhances the mechano-chemical degradation of polypropylene. Chemical Science. doi:10.1039/d5sc03348a
  2. Hergesell, A. H.; Baarslag, R. J.; Seitzinger, C. L.; Meena, R.; Schara, P.; Tomović, Ž.; Li, G.; Weckhuysen, B. M.; Vollmer, I. (2024). Surface-activated mechano-catalysis for ambient conversion of plastic waste. Journal of the American Chemical Society. doi:10.1021/jacs.4c07157
  3. Zhang, H.; Bolshakov, A.; Meena, R.; Garcia, G. A.; Dugulan, A. I.; Parastaev, A.; Li, G.; Hensen, E. J. M.; Kosinov, N. (2023). Revealing active sites and reaction pathways in methane non-oxidative coupling over iron-containing zeolites. Angewandte Chemie International Edition. doi:10.1002/anie.202306196
  4. Simi, S.; Mathew, T.; Meena, R.; Li, G.; Shiju, N. R.; et al. (2025). Towards improved activity and stability in RWGS reaction: dispersed copper in mesoporous alumina matrix. Chemical Engineering Journal. doi:10.1016/j.cej.2025.169863
  5. Pirgach, D. A.; Meena, R.; Li, G.; Miloserdov, F. M.; van Es, D. S.; Bruijnincx, P. C. A.; Bitter, J. H. (2025). Medium-dependent regioselectivity of electrochemical bromination of methyl levulinate. RSC Sustainability. doi:10.1039/d5su00037h
  6. Ghorai, S.; Meena, R.; Joseph, A. P.; Jemmis, E. D. (2021). Comparison of RNC coupling and CO coupling mediated by Cr–Cr quintuple bond and B–B multiple bonds. Journal of Physical Chemistry A. doi:10.1021/acs.jpca.1c05185

Technical Skills

DFT codes Quantum ESPRESSO, VASP, CP2K, GAUSSIAN
Catalysis & surface science Surface reaction mechanisms, transition-state searches (NEB), defect and dopant screening, microkinetic modelling, thermochemistry
Beyond DFT Ab initio and ML-accelerated molecular dynamics, metadynamics (CP2K + PLUMED), Quantum Monte Carlo (TurboRVB)
ML potentials Training and validation of MLIPs (MACE, fair-chem/UMA); active-learning loops with DFT reference data
HPC & workflows SLURM, modular Python/Bash pipelines for high-throughput DFT and AIMD, Materials Project + ASE-based automation
Programming Python (NumPy, ASE, scikit-learn, pandas, matplotlib), Bash, Git, Linux
ML / data science SISSO (symbolic regression), SHAP-based explainable AI, feature engineering, LLM-driven workflow automation (API, tool calling, agents)

Funding and Awards

2021 – 2026

NWO HPC grants, Snellius supercomputer

12.5M CPU and 50k GPU hours (€200k equivalent); independently authored compute proposals

2019

Erasmus+ Fellowship, Sorbonne Université

€10k — awarded for M2 studies in France

2015 – 2020

INSPIRE Scholarship, Government of India

Nationally competitive science scholarship; 1,000 awards per year

2013 – 2015

NTSE Scholarship, Government of India

Nationally competitive talent-search scholarship; 1,000 awards per year

Teaching and Supervision

  • Co-taught advanced computational chemistry to graduate students at Wageningen University, 2020–2025.
  • Supervised 6 thesis projects: 3 MSc major, 2 MSc minor, 1 BSc.
  • Served as group resource on DFT methods and HPC workflows.
  • Supervised undergraduate organic chemistry practicals.

Professional Development

  • Python for Data Science and Machine Learning Bootcamp, Udemy, 2025.
  • CECAM "Understanding Molecular Simulation" school, University of Amsterdam, 2023.
  • Scientific Writing, Wageningen in'to Languages, 2023.
  • Project and Time Management, Wageningen Graduate School, 2023.
  • Paris International School on Advanced Computational Materials Science, Sorbonne University, 2021.
  • Han-sur-Lesse winter school for theoretical and computational chemistry, Ardennes, 2021.

Languages

EnglishFluent
HindiNative
DutchA2

References

Available on request from the advisors listed in this CV.