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arXiv:2412.00951

A Quantum Computing Approach to Simulating Corrosion Inhibition

Karim Elgammal, Marc Maußner

Published Dec 1, 2024• 1 citation
arXivHTMLPDF

Abstract

This work demonstrates a systematic implementation of hybrid quantum-classical computational methods for investigating corrosion inhibition mechanisms on aluminum surfaces. We present an integrated workflow combining density functional theory (DFT) with quantum algorithms through an active space embedding scheme, specifically applied to studying 1,2,4-Triazole and 1,2,4-Triazole-3-thiol inhibitors on Al111 surfaces. Our implementation leverages the ADAPT-VQE algorithm with benchmarking against classical DFT calculations, achieving binding energies of -0.386 eV and -1.279 eV for 1,2,4-Triazole and 1,2,4-Triazole-3-thiol, respectively. The enhanced binding energy of the thiol derivative aligns with experimental observations regarding sulfur-functionalized inhibitors' improved corrosion protection. The methodology employs the orb-d3-v2 machine learning potential for rapid geometry optimizations, followed by accurate DFT calculations using CP2K with PBE functional and Grimme's D3 dispersion corrections. Our benchmarking on smaller systems reveals that StatefulAdaptVQE implementation achieves a 5-6x computational speedup while maintaining accuracy. This work establishes a workflow for quantum-accelerated materials science studying periodic systems, demonstrating the viability of hybrid quantum-classical approaches for studying surface-adsorbate interactions in corrosion inhibition applications. In which, can be transferable to other applications such as carbon capture and battery materials studies.

DOI
10.48550/arxiv.2412.00951

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This work demonstrates a systematic implementation of hybrid quantum-classical computational methods for investigating corrosion inhibition mechanisms on aluminum surfaces. We present an integrated workflow combining density functional theory (DFT) with quantum algorithms through an active space embedding scheme, specifically applied to studying 1,2,4-Triazole and 1,2,4-Triazole-3-thiol inhibitors on Al111 surfaces. Our implementation leverages the ADAPT-VQE algorithm with benchmarking against classical DFT calculations, achieving binding energies of -0.386 eV and -1.279 eV for 1,2,4-Triazole and 1,2,4-Triazole-3-thiol, respectively. The enhanced binding energy of the thiol derivative aligns with experimental observations regarding sulfur-functionalized inhibitors' improved corrosion protection. The methodology employs the orb-d3-v2 machine learning potential for rapid geometry optimizations, followed by accurate DFT calculations using CP2K with PBE functional and Grimme's D3 dispersion corrections. Our benchmarking on smaller systems reveals that StatefulAdaptVQE implementation achieves a 5-6x computational speedup while maintaining accuracy. This work establishes a workflow for quantum-accelerated materials science studying periodic systems, demonstrating the viability of hybrid quantum-classical approaches for studying surface-adsorbate interactions in corrosion inhibition applications. In which, can be transferable to other applications such as carbon capture and battery materials studies.
This work demonstrated a proof-of-concept use case for the emerging quantum-centric supercomputing approaches combining HPC resources with quantum computers. We presented a systematic implementation of hybrid quantum-classical computational method for accelerating atomistic simulations studying corrosion inhibition. We used aluminium surface with screened inhibitor molecule on top. We combined density functional theory (DFT) with quantum algorithm through an active space embedding scheme. We chosen inhibitor molecules of 1,2,4-Triazole and 1,2,4-Triazole-3-thiol. Our implementation leveraged the ADAPT-VQE algorithm with benchmarking against classical DFT calculations, achieving binding energies of -0.386 eV and -1.279 eV for 1,2,4-Triazole and 1,2,4-Triazole-3-thiol, respectively. The binding energy of the thiol derivative aligned with experimental observations regarding sulfur-functionalized inhibitors' which could improve corrosion protection. The methodology employed the orb-d3-v2 machine learning potential for rapid geometry optimizations, followed by accurate DFT calculations using CP2K with PBE functional and Grimme's D3 dispersion corrections. CP2K is a robust DFT package that can scale on HPC classical resources of CPUs and GPUs efficiently. We also benchmarked against smaller systems and revealed that StatefulAdaptVQE implementation achieves a 5-6×computational speedup while maintaining accuracy. This work contributes to the literature studying quantum-accelerated materials science applied to periodic systems, demonstrating the viability of hybrid quantum-classical approaches for studying surface-adsorbate interactions in corrosion inhibition applications. In which, can be transferable to other applications such as carbon capture on metal oxide frameworks and solid-state battery materials studies.
Corrosion is a pervasive issue that impacts the structural integrity and performance of materials across various industries, imposing a significant economic impact globally. In fields like aerospace and defense, developing corrosion-resistant materials is critical, but progress is often hindered by the complexities of material-environment interactions. While computational methods have advanced in designing corrosion inhibitors and corrosion-resistant materials, they fall short in understanding the fundamental corrosion mechanisms due to the highly correlated nature of the systems involved. This paper explores the potential of leveraging quantum computing to accelerate the design of corrosion inhibitors and corrosion-resistant materials, with a particular focus on magnesium and niobium alloys. We investigate the quantum computing resources required for high-fidelity electronic ground-state energy estimation (GSEE), which will be used in our hybrid classical-quantum workflow. Representative computational models for magnesium and niobium alloys show that 2292 to 38598 logical qubits and (1.04 to 1962) × 10 13 T-gates are required for simulating the ground-state energy of these systems under the first quantization encoding using plane waves basis.
Corrosion inhibition efficiency (IE%) prediction plays a central role in the computational discovery of high-performance organic inhibitors. Classical machine learning has shown promising results; however, its performance often deteriorates when learning non-linear interactions between quantum chemical descriptors. Meanwhile, quantum machine learning (QML) provides enhanced expressivity through quantum feature mapping but remains limited by NISQ-era hardware. In this study, we propose a Hybrid Quantum Neural Network (HQNN) integrating classical dense layers with variational quantum circuits (VQC) to predict the inhibition efficiency of organic corrosion inhibitors. Using a curated dataset of 660 molecules with DFT descriptors, the HQNN achieves an RMSE of 3.41 and R² of 0.958, outperforming classical regressors and pure VQC. The results demonstrate that hybrid quantum models offer a balanced trade-off between quantum advantage and practical feasibility in materials informatics.
High Resolution Image Download MS PowerPoint Slide This study explores the application of quantum computing to metal cluster analysis through the development and implementation of a quantum-DFT embedding workflow. Classical computational methods, while transformative, often face limitations in achieving chemical accuracy and computational efficiency, particularly for nanoscale systems. To address these challenges, we integrate the Variational Quantum Eigensolver (VQE) with density functional theory (DFT), leveraging the capabilities of quantum computing aiming to improve the modeling of electronic structures. Aluminum and gold clusters were used as model systems to test the established workflow. The workflow successfully determined electronic properties for aluminum clusters up to Al 7 – . Although gold clusters were used as a test case to investigate the potential reduction of nitric oxide (NO), memory limitations, the lack of relativistic corrections, and the inability to handle open-shell systems presented challenges that underscore the need for advancements in quantum hardware and algorithms. This proof-of-concept study demonstrates the potential of quantum DFT embedding to advance materials discovery, including applications in catalysis and nanomaterial design, while providing insights into the current limitations of near-term quantum devices.
We present a hybrid classical-quantum computational pipeline for the determination of adsorption energies of a benzotriazole molecule on an aluminum alloy surface relevant for the transport industry, in particular to address the corrosion problem. The molecular adsorbate and substrate alloy were selected by interrogating molecular and materials databases, in search for desired criteria. The protocol can be generalized to other surfaces with arbitrary orientation and chemical composition, as well as to other molecular adsorbates. It includes three main steps based on mean-field electronic structure calculations, embedding theories and quantum algorithms. The quantum computing step demonstrated here with the variational quantum eigensolver is amenable to any other reliable quantum algorithm for ground-state energy estimation. Excited-state energies can also be taken into account in the quantum computing step, if the target reaction involves excited states.
Understanding strongly correlated systems is essential for advancing quantum chemistry and materials science, yet conventional methods like Density Functional Theory (DFT) often fail to capture their complex electronic behavior. To address these limitations, we develop a hybrid quantum-classical framework that integrates Multiconfigurational Self-Consistent Field (MCSCF) with the Variational Quantum Eigensolver (VQE). Our initial benchmarks on water dissociation enabled the systematic optimization of key computational parameters, including ansatz selection, active space construction, and error mitigation. Building on this, we extend our approach to investigate the interactions between graphene analogues and water, demonstrating that our framework produces binding energies consistent with high-accuracy quantum methods. Furthermore, we apply this methodology to predict the binding energies of transition metals (Fe, Co, Ni) on both pristine and defective graphene analogues, revealing strong charge transfer effects and pronounced multireference character─phenomena often misrepresented by standard DFT. In contrast to many existing quantum algorithms that are constrained to small molecular systems, our framework achieves chemically accurate predictions for larger, strongly correlated systems such as metal-graphene complexes. This advancement highlights the capacity of hybrid quantum-classical approaches to address complex electronic interactions and demonstrates a practical route toward realizing quantum advantage for real-world materials applications in the Noisy Intermediate-Scale Quantum (NISQ) era.