Implementing a Variational Quantum Eigensolver (VQE) to simulate the interactions between Metal-Organic Frameworks (MOFs) and Carbon Dioxide molecules to simulate Carbon Capture.

Harnessing quantum computing to accelerate the discovery of next-generation metal-organic frameworks (MOFs) for efficient carbon dioxide capture.

Quantum Computing promises a new era of computing that uses the quantum nature of a particle to perform computations exponentially faster. With climate change and greenhouse gases like Carbon Dioxide at the forefront of global concerns, carbon capture has emerged as a critical area of research. Metal-organic frameworks (MOFs) are highly porous materials capable of selectively adsorbing CO₂, making them prime candidates for sustainable solutions. However, the vast design space of MOFs makes discovery a challenge — this is where quantum concepts like the Variational Quantum Eigensolver (VQE) comes in.

This project integrates quantum simulation and machine learning to:

• Simulate MOFs using the Variational Quantum Eigensolver (VQE) algorithm.
• Estimate ground-state energies to assess CO₂ capture efficiency.
• Train an AI model to predict and propose novel MOF structures optimized for carbon capture.
• Test out the proposed MOF structures on the VQE algorithm to analyze ground state energy.
• Output predictions, highlighting performance metrics like uptake, selectivity, and heat of adsorption.

1. CIF Parsing: Structural data from a real MOF Dataset (in .cif format) is parsed.
2. Hamiltonian Construction: A simplified 2-qubit molecular Hamiltonian is built for each structure.
3. Quantum Simulation: Using PennyLane, VQE estimates the ground state energy of each MOF-CO₂ system.
4. AI Model: A neural network is trained on simulation outputs and pre-existing MOF dataset to propose new MOF structures.
5. Results: Energies and material properties are saved in .csv format and visualized via plots.

• NumPy: Fundamental package for numerical computations in Python.
• Pandas: Data structures and data analysis tools.
• PyTorch: Deep learning framework for building and training neural networks.
• scikit-learn: Machine learning library offering tools for data preprocessing, classification, regression, and more.
• Matplotlib: Comprehensive library for creating static, animated, and interactive visualizations.
• os: Module providing a way of using operating system-dependent functionality.

• PennyLane: Cross-platform Python library for quantum computing and machine learning, enabling hybrid quantum-classical computations.

• CUDA Toolkit: NVIDIA's parallel computing platform and programming model for GPU acceleration.

• React: JavaScript library for building user interfaces.
• JavaScript: Programming language that enables interactive web pages.

• Anaya, Alan, and Francisco Delgado. "Simulating Molecules Using the VQE Algorithm on Qiskit." ArXiv, 8 Jan. 2022, arxiv.org/pdf/2201.04216.
• Ramesh Dahale, Gopal. "Quantum Simulations for Carbon Capture on Metal-Organic Frameworks." ArXiv, 21 Nov. 2023, arxiv.org/pdf/2311.12411.

• Gran, Alain Delgado. "A Brief Overview of VQE." PennyLane Demos, Xanadu, 8 Feb. 2020, pennylane.ai/qml/demos/tutorial_vqe.
• "Textbook/Notebooks/Ch-Applications/Vqe-Molecules.ipynb at Main · Qiskit/Textbook." GitHub, github.com/Qiskit/textbook/blob/main/notebooks/ch-applications/vqe-molecules.ipynb.
• "Variational Quantum Eigensolver." Catalyst, 2023, docs.pennylane.ai/projects/catalyst/en/latest/demos/adaptive_circuits_demo.html.