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👋 A Postdoctoral Research Associate at Los Alamos National Laboratory (LANL) 🚀, working at the intersection of machine learning interatomic potentials (MLIPs), graph neural networks (GNNs), and nonadiabatic molecular dynamics.

I build AI surrogates and research-grade scientific software that make quantum-chemistry simulations tractable at scale: replacing expensive ab initio calculations with uncertainty-aware ML models for ground- and excited-state dynamics, then taking those models from prototype to reproducible, GPU-distributed production pipelines.

What I work on:

• 🤖 MLIPs & Active Learning: HIP-NN / hippynn architectures with uncertainty quantification for automated nonadiabatic AI-MD (I contribute to the hippynn ecosystem)
• ⚡ Excited-State Dynamics: Trajectory surface hopping (FSSH), multi-state PES, phase tracking for nonadiabatic coherence
• 🔬 Hybrid QM/MM & Spectroscopy: Absorption and fluorescence spectroscopy in explicit environments; ensemble Franck-Condon methods (contributor to the MolSpecPy spectroscopy package)
• 💊 Drug Discovery: Protein-ligand binding free-energy pipelines (QM/MM, MM-PBSA), BTK inhibitor modeling at Frontier Medicines
• 📐 Materials Discovery: Excitonic coupling in π-stacked aggregates; energy/charge transfer via Marcus, Förster, and Redfield theories
• 🖥️ Scientific Software & HPC: Research-to-production pipelines: high-throughput excited-state dataset generation across multiple quantum backends, distributed GPU training (Ray), containerization (Docker · Singularity · HPC Container Maker), and SLURM orchestration

Stack: HIP-NN · hippynn · PyTorch · NEXMD · CUDA · Ray · PySEQM · GPU4PySCF · Q-Chem · OpenMM · TeraChem · ORCA · AMBER · ASE · RDKit · SLURM · Docker · Singularity

📍 Los Alamos, NM · 📅 November 2024 – Present · Advisor: Dr. Sergei Tretiak

Building next-generation AI surrogates and machine learning interatomic potentials (MLIPs) to overcome computational bottlenecks in nonadiabatic molecular dynamics — enabling energy-transfer simulations in large-scale molecular systems previously beyond the reach of quantum chemistry.

Key contributions:

• 🤖 Uncertainty-Aware MLIPs: Active learning pipelines built on HIP-NN / hippynn / GNN architectures for automated ground, adiabatic, and nonadiabatic AI-MD with out-of-distribution detection and data-efficient training that approaches ab initio accuracy from far fewer sampled configurations
• ⚡ Surrogate Models for Multi-State Dynamics: ML surrogates for excited-state PES using trajectory surface hopping (FSSH) with rigorous phase tracking across S0–S3 states
• 📚 High-Throughput Dataset Generation: Sole author of a molecule-agnostic, SLURM-aware workflow engine that builds excited-state MLIP training datasets across three interchangeable quantum backends (semiempirical AM1, DFT/TDDFT) with multi-GPU parallelism and restart-safe scheduling
• 🖥️ Research-to-Production Software: Distributed GPU training with Ray, containerized environments (Docker · Singularity · HPC Container Maker), and SLURM-orchestrated campaigns across multiple HPC clusters
• 🧪 Excitonic Coupling in PDI Aggregates: Quantitative structure-property relationships in perylene diimide (PDI) trimers — covalent tethering controls electronic coupling for organic electronics. Published in Nano Letters (Top 10%, 2026)
• 🌀 X-ray Circular Dichroism: Mechanistic framework for chiroptical signal design in azobenzene derivatives. J. Phys. Chem. Lett. (Cover Article, Top 15%, 2025)
• 🎙️ MLCM-26: Co-organizing Machine Learning in Chemical & Materials Sciences 2026 — Santa Fe, NM · May 18–21 · $50K+ raised · 40 speakers · 100 participants

HIP-NN hippynn PyTorch NEXMD CUDA Ray FSSH GNNs Active Learning SLURM Docker

📍 Merced, CA · 📅 August 2018 – August 2024 · Advisor: Prof. Christine Isborn

Engineered high-fidelity QM/MM computational workflows for simulating absorption and fluorescence spectra of chromophores in explicit solvent — solving the long-standing challenge of capturing both vibronic coupling and environmental broadening simultaneously.

Key contributions:

• 📊 Ensemble Franck-Condon Spectroscopy: First extension of E-FC methods to fluorescence spectra; first direct comparison of all three E-FC variants across NBD, Nile Red, and 7MC in explicit QM/MM solvent. Demonstrated Eopt-avgFTFC achieves gold-standard accuracy at 25% of computational cost. Contributor to the MolSpecPy spectroscopy package. Published in J. Chem. Phys. (2024)
• 💡 Polaritonic Chemistry ($7.5M DOD-funded): Computed excited-state energies, transition dipole moments, and Franck-Condon spectra for J- and H-type molecular aggregates in collaboration with experimental groups at UC San Diego and Penn State. Published in Nature Communications (2022)
• 🔬 Explicit-Solvent QM/MM Pipelines: Full AIMD sampling using TeraChem + AMBER (GAFF2) with electrostatic embedding, CAM-B3LYP/6-31G(d) + TDA-TDDFT, 100 uncorrelated snapshots per trajectory

QM/MM AIMD TeraChem AMBER TDDFT Franck-Condon Gaussian OpenMM Python

📍 South San Francisco, CA · 📅 May 2023 – August 2023 · Manager: Monika Williams

Applied hybrid QM/MM methods to accelerate structure-based drug discovery pipelines at a leading targeted-protein-degradation biotech.

Key contributions:

• 🐍 Automated Pipeline: Python workflow for SMILES-to-desolvation energy calculations and conformational sampling for BTK inhibitors using OpenMM + TeraChem QM/MM interface
• ⚖️ Binding Free Energy: QM/MM free-energy pipelines for accurate rank-ordering of BTK inhibitors.
• 🔩 Molecular Docking: Biased and unbiased ligand-based docking using MOE across multiple BTK inhibitor scaffolds

OpenMM TeraChem MOE RDKit QM/MM Python Free Energy Structure-Based Drug Design BTK

📍 Bangalore, India · 📅 January 2018 – July 2018 · Advisor: Prof. Biman Bagchi

Investigated Förster Resonance Energy Transfer (FRET) beyond the classical point-dipole approximation — targeting a fundamental limitation in how excitation energy transfer is modeled in biological imaging and solar-energy systems.

Key contributions:

• 🔬 Beyond Förster Theory: Relaxed point-dipole and fixed-orientation-factor constraints for more realistic energy-transfer modeling in donor-acceptor systems
• 🌈 Spectral Benchmarking: TDDFT excited-state geometry optimization and absorption/fluorescence spectra for AlexaFluor-488/594 (bioimaging) and Triphenylamine-Rhodamine (dye-sensitized solar cells) in DMSO and DCM
• 📐 DFT Functional Benchmarking: Demonstrated basis-set effects were minimal for rigid dye systems; identified B3LYP as optimal functional achieving close agreement with experimental spectra

Gaussian TDDFT B3LYP DFT Python Implicit Solvent FRET Excited-State

📊 Google Scholar · 🔬 ResearchGate · 📄 ORCID: 0000-0002-8313-1393

0. 🔄 Building Uncertainty-Driven Machine Learned Interatomic Potentials for Automated Non-Adiabatic Molecular Dynamics

Ajay Khanna, ..., and Sergei Tretiak

Designing uncertainty-aware MLIPs with active-learning pipelines built on GNN/MPNN architectures for automated ground, adiabatic, and nonadiabatic AI-MD — enabling reliable out-of-distribution detection for multi-state molecular dynamics simulations.

MLIPs GNNs Uncertainty Quantification Active Learning Nonadiabatic MD HIP-NN

1. Covalent Control of Excitonic Interactions in Perylene Diimide Trimers: A Computational Study

Ajay Khanna, Jean-Hubert Olivier, Sebastian Fernandez-Alberti, and Sergei Tretiak, Nano Letters, 2026

Established quantitative structure-property relationships in π-stacked PDI trimers (Free, Sandwich, Zigzag configurations), demonstrating how covalent tethering strategies control electronic coupling heterogeneity — a design blueprint for next-generation organic electronics and photovoltaics.

Excitonic Coupling PDI Aggregates π-Stacking Organic Electronics TDDFT Energy Transfer

2. Deconstructing Chirality: Probing Local and Non-local Effects in Azobenzene Derivatives with X-ray Circular Dichroism

Ajay Khanna, Victor M. Freixas, Lei Xu, Niri Govind, Jeremy R. Rouxel, Marco Garavelli, Shaul Mukamel, and Sergei Tretiak, Journal of Physical Chemistry Letters, **Cover Article, 2025

Developed a site-specific X-ray spectroscopy framework to decouple local vs. non-local chiroptical contributions in azobenzene derivatives — a mechanistic foundation for the rational design of functional molecular machines and chiral materials.

X-ray Circular Dichroism Chirality Azobenzene Molecular Machines Chiroptical NEXMD

3. Calculating Absorption and Fluorescence Spectra for Chromophores in Solution with Ensemble Franck-Condon Methods

Ajay Khanna, Sapana V. Shedge, Tim J. Zuehlsdorff, and Christine M. Isborn, Journal of Chemical Physics, 2024

First extension of ensemble Franck-Condon (E-FC) methods to fluorescence spectra; first direct comparison of all three E-FC variants across NBD, Nile Red, and 7MC in explicit QM/MM solvent. Key finding: Eopt-avgFTFC achieves gold-standard accuracy at 25% of the computational cost.

QM/MM Franck-Condon Fluorescence Absorption Spectroscopy AIMD Vibronic Effects TeraChem

Degree	Institution	Focus
🔬 Ph.D., Computational Chemistry	University of California, Merced	Hybrid QM/MM · AIMD · Fluorescence Spectroscopy · Excited-State Dynamics · Resonance Energy Transfer
⚗️ M.Sc., Chemistry	National Institute of Technology, Rourkela	Computational Chemistry · Zintl Clusters · Advanced Experimental Chemistry
🔭 B.Sc. (Hons.), Chemistry	University of Delhi	Physical · Organic · Inorganic Chemistry

Certification	Issuing Organization	Year	Skills
🤖 Microsoft AI & ML Engineering	Coursera	2026	AI Engineering · ML Pipelines · Model Deployment
🧪 Introduction to Cheminformatics & Medicinal Chemistry	Udemy	2023	Cheminformatics · Drug Discovery · QSAR · RDKit
📊 Data Science with Python	Simplilearn	2022	Python · Data Analysis · Scikit-learn · Pandas
🚀 Fundamentals of Accelerated Computing with CUDA Python	NVIDIA	2022	CUDA · GPU Programming · Parallel Computing · Numba

A modular Python engine for multi-trajectory NVE/NVT molecular-dynamics benchmarking of MLIPs against quantum-chemistry references, built on ASE and PySEQM. Designed for systematic evaluation of machine learning interatomic potentials across ground and excited states. Releasing as open source alongside an upcoming publication.

Key features:

• Multi-trajectory NVE/NVT ensemble comparison against ab initio references
• Quantitative agreement metrics: KL divergence, Wasserstein distance
• Publication-quality plotting pipeline
• Modular architecture for extension to new MLIP frameworks

MLIPs ASE PySEQM Benchmarking NVE/NVT Python NumPy Matplotlib

Co-organizing an international conference at the intersection of machine learning, chemistry, and materials science.

• 40 invited speakers from academia, national labs, and industry
• 100 participants · Intentionally small for deep scientific exchange
• Rising Star ⭐ talks for early-career researchers
• GPU compute awards + SCM Research Excellence Awards

MLCM26 LANL MLIPs GNNs Materials Science Computational Chemistry Conference

A molecule-agnostic, SLURM-aware workflow engine (sole-authored) that generates machine-learning interatomic potential training datasets for multistate, multitask excited-state prediction — from an unoptimized geometry all the way to assembled dataset arrays. Releasing as open source alongside an upcoming project.

Key features:

• End-to-end orchestration: Gaussian ground-state optimization and TDA/CAM-B3LYP excited-state screening → NEXMD AM1 dynamics → frame extraction → excited-state single points → dataset assembly
• Three interchangeable quantum backends behind one interface: PySEQM (CIS/AM1 semiempirical, GPU), Q-Chem (DFT/TDA, CPU SLURM arrays), and GPU4PySCF (DFT/TDA/NACV, multi-GPU) — all emitting the same canonical dataset arrays
• Multi-GPU parallelism with per-device workers, including MIG-partitioned A100 support; restart-safe SLURM arrays and job monitoring (sbatch/sacct/squeue)
• Config-driven CLI with dry-run, per-stage restart (--continue), and transparent, inspectable generated SLURM files

Python CUDA SLURM HPC PySEQM GPU4PySCF Q-Chem NEXMD DFT/TDDFT MLIP Datasets

Last updated: August 2026