This page lists my peer-reviewed articles, preprints, and other research outputs in quantum chemistry, machine learning, molecular dynamics, and high-performance computing. These works represent my contributions to developing new methods and applying computational approaches to challenging problems in physical and computational science.
Developed a GPU-parallelized framework "PySCES") for simulating ultrafast photochemical reactions, enabling large-scale, high-throughput modeling of excited-state charge transfer in realistic materials with major runtime reductions.
Nonadiabatic semiclassical dynamics; GPU/HPC parallelization & workflow design; Python-based scientific software engineering.
Revealed how different hydrogen-bonding environments directly influence optical line-shapes in solvated dyes, offering a pathway to better predicting solvent effects in optical spectroscopy.
Quantum chemistry/QM/MM excited-state modeling; molecular dynamics & Fourier spectral analysis; hydrogen-bond network interpretation & data visualization.
Combined MD simulations and DLS experiments to uncover that the novel CF₃SF₄-ethanol aggregates at substantially lower concentrations compared to conventional fluorinated solvents—demonstrating its potential as an environmentally benign yet highly effective green solvent for stabilizing biomolecules and enhancing formulation strategies.
Molecular dynamics modeling, DFT-based force-field parameterization, solvent aggregation analysis, translating computational insight into greener materials and process design.
Introduces a next-generation energy model that replaces conventional fixed-charge force fields with a site‑centered density representation – capturing charge penetration effects in RNA interactions to enable more accurate and scalable biomolecular simulations, offering a robust foundation for improved force fields in biotech and drug-discovery pipelines.
Force field innovation, electrostatics modeling beyond fixed-partial-charge approaches, hybrid QM-inspired model development, scalable simulation strategy design for computational biology.
Combined experiment and simulation to identify chemical pathways that drive environmental degradation in hybrid semiconductors, informing strategies for longer-lasting photovoltaic and optoelectronic devices.
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Evaluated how molecular dynamics and experimental data can be combined to better understand RNA modifications, with implications for designing therapeutics targeting RNA structure and function.
Biomolecular simulation, force field assessment, validation against experimental datasets, workflow optimization.
Created an OpenMP parallelized simulator for ion mobility spectrometry - mass spectrometry experiments, enabling rapid analysis of molecular collision cross-sections for complex chemical and biological systems.
Algorithm design, parallel computing, computational mass spectrometry, performance engineering, interdisciplinary experimental–computational research.
Quantified how single-stranded DNA interacts with graphene surfaces, guiding the engineering of next-generation biosensors and nanomaterial-based diagnostics.
Thermodynamics modeling, data analysis, interdisciplinary experimental–computational research, force-field development
Developed a hybrid method that bridges DFT-level accuracy and classical molecular mechanics to model RNA nucleobase behavior at scale – enabling precise yet computationally efficient simulations tailored to high-throughput screening in biotech and pharmaceutical research.
Quantum chemistry (DFT), force-field development, QM/MM hybrid methodology design, scaling biomolecular simulation workflows for industrial R&D.