Dr. Viki Kumar Prasad
Positions
Assistant Professor
Assistant Professor
Faculty of Science, Institute for Quantum Science and Technology
Contact information
Phone number
Office: 403.220.6881
Location
Office: SB 327
Lab: SB 326
I'm looking for...
Research partners
Interested in:
- collaborating on classical and quantum machine learning
- creating quantum computing use-cases
- requiring quantum chemical modeling and simulation
- complementing experimental chemistry
Funding
Interested in:
- sponsoring R&D activities
- providing matching contributions
- requiring consultation
- creating outreach activities
Background
Educational Background
PhD Theoretical and Quantum Chemistry, University of British Columbia, Kelowna, Canada,
MSc Chemistry, Indian Institute of Technology, Kharagpur, India,
BSc Chemistry, St. Xavier's College, Kolkata, India,
Biography
Viki Kumar Prasad joined the Department of Chemistry at the University of Calgary in July 2024. As the principal investigator of the Prasad Research Group, he currently oversees research in the development and application of novel computational chemistry methodologies. Prior to his appointment, Viki served as a postdoctoral researcher at the University of Toronto from 2022 to 2024, working in Hans-Arno Jacobsen's Middleware Systems Research group. During his postdoctoral research, he explored the application of quantum computing to predict chemical properties in an interdisciplinary collaboration. With the Data Sciences Institute postdoctoral fellowship, he continued his work at the University of Toronto, focusing on quantum machine learning methods for chemistry. Previous to this, Viki obtained his PhD in theoretical and quantum chemistry in 2021 from the University of British Columbia supervised by Gino DiLabio and co-supervised by Alberto Otero-de-la-Roza (University of Oviedo). His doctoral thesis focused on developing new quantum chemical methods for the efficient and accurate electronic structure modeling. In 2016, he completed his MSc at the Indian Institute of Technology (Kharagpur), working with Anoop Ayyappan. He earned his BSc in 2014 from St. Xavier's College (Kolkata). Additionally, Viki had a brief postdoctoral experience in early 2022 in computational materials science at the University of British Columbia, participating in a collaborative project with Robert Szilagyi and the "AtomDec" consortium to design carbon-based materials. He also spent time at the University of Melbourne in the summer of 2016, working under Lars Goerigk's supervision.
Research
Areas of Research
Participation in university strategic initiatives
Projects
Our research focuses on advancing chemistry through the application of quantum computing principles, particularly by developing quantum machine learning (QML) models that distribute workloads between classical and quantum computers. We are exploring the feasibility of quantum enhancement in building such QML models for various tasks, aiming to understand how quantum computing offers new ways to process data and build more efficient models with less data. Our work investigates the potential improvements QML can bring in predicting molecular properties and generating chemical data, while also developing innovative search strategies to discover better-performing models. Additionally, we are focused on quantum resource optimization, as current quantum computers are still in their early stages and face limitations, including limited processing capacity and susceptibility to errors. To address these challenges, we are interested in creating techniques and algorithms that optimize quantum devices for chemical applications. Our efforts include minimizing quantum processing requirements, accelerating parameter tuning, enhancing control through adjustable parameters, implementing error mitigation strategies, distributing workloads across multiple quantum devices, and improving task distribution between classical and quantum resources.
By combining machine learning algorithms with computational chemistry, our work involves developing models that can provide fast and accurate molecular property predictions, overcoming the computational challenges of conventional quantum mechanical (QM) methods. While machine learning reduces computational costs, it often faces issues with poor generalization and limited training data. To address these challenges, we are interested in integrating QM information into the training step, developing correction models for less accurate QM methods, and creating large, high-quality datasets using in-house approaches to improve model performance. These efforts can enable rapid, accurate predictions of chemical properties for various applications. For instance, we are developing machine learning models to predict bond dissociation enthalpies, which is crucial for determining chemical bond strengths and providing insights into molecular stability, facilitating the design of organic antioxidants, and identification of drug autooxidation sites. We are also developing a machine learning-driven synthesis planning tool aimed at identifying cost-effective and sustainable synthetic routes, reducing the experimental reliance on trial-and-error. Additionally, we are developing models to predict transition states—key structures in chemical reactions—much faster and more efficiently than traditional methods.
Our research in quantum chemistry involves advancing the accuracy and efficiency of electronic structure methods for performing numerous computations in a short period or handling large molecular systems. By developing and optimizing atom-centered potentials (ACP), we bridge the gap between computationally inexpensive quantum mechanical methods and the high-level accuracy required for high-throughput applications. ACP can correct the limitations of small-basis-set Hartree–Fock (HF) and density functional theory (DFT) methods, enabling accurate predictions of molecular properties such as non-covalent interaction energies, reaction barriers, bond dissociation energies, and molecular geometries. This approach supports the efficient modeling of complex molecular systems with hundreds of atoms while retaining computational feasibility. Additionally, we integrate ACP with semi-empirical corrections like D3 to enhance the accuracy and applicability of HF and DFT methods for biochemical and organic chemistry applications. By generating large, high-quality datasets through ACP-driven workflows, we also advance deep learning models for quantum chemistry, creating tools that achieve near coupled-cluster level of theory accuracy at a reduced computational cost.
We harness the power of data for chemistry by creating diverse benchmark data sets of molecular properties, developing and undertaking use-case chemical applications (reaction discovery, catalyst optimization, covalent drug design,...), and providing computational screening support to experimental collaborations. Our goal is to leverage data-driven approaches to uncover new insights and accelerate discoveries, embracing a modern, digital approach to chemistry. By addressing the gaps in existing reference data sets, we have developed high-quality resources such as BH9, BSE49, and PEPCONF. These data sets provide consistent benchmarks for barrier heights, reaction energies, bond dissociation reactions, and peptide conformers, setting new standards for evaluating or developing computational methodologies. They eliminate the variability and uncertainty of data from disparate sources, offering researchers reliable tools to assess and refine their methods. In experimental collaborations, we are investigating the reaction energetics of a large pool of candidates for a particular organic reaction type to identify novel synthetic targets. We are interested in developing machine learning models that leverage this data to enable high-throughput exploration of chemical reaction space.
Awards
- Data Sciences Institute Postdoctoral Fellowship, University of Toronto. 2023
- PeerJ Best Poster Award, 12th Triennial Congress, World Association of Theoretical and Computational Chemists. 2022
- Chemical Computing Group Excellence Award, Chemical Computing Group, Inc. (CCG). 2020
Publications
- Bridging the gap between high-level quantum chemical methods and deep learning models. VK Prasad, A Otero-de-la-Roza, GA DiLabio. Machine Learning: Science and Technology. 5 (1), 015035. (2024)
- Applications of noisy quantum computing and quantum error mitigation to “adamantaneland”: a benchmarking study for quantum chemistry. VK Prasad, F Cheng, U Fekl, HA Jacobsen. Physical Chemistry Chemical Physics. 26 (5), 4071-4082. (2024)
- Heteroatom-vacancy centres in molecular nanodiamonds: a computational study of organic molecules possessing triplet ground states through σ-overlap. CM Macarios, J Pittner, VK Prasad, U Fekl. Physical Chemistry Chemical Physics. 26 (39), 25412-25417. (2024)
- Quantitative and qualitative analysis of nitrogen species in carbon at the ppm level. T Yoshii, G Nishikawa, VK Prasad, S Shimizu, R Kawaguchi, R Tang, K Chida, N Sato, R Sakamoto, K Takatani, D Moreno-Rodríguez, P Škorňa, E Scholtzová, RK Szilagyi, H Nishihara. Chem (Cell Press). 10 (8), 2450-2463. (2021)
- Small-basis set density-functional theory methods corrected with atom-centered potentials. VK Prasad, A Otero-de-la-Roza, GA DiLabio. Journal of Chemical Theory and Computation. 18 (5), 2913-2930. (2022)
- Fast and accurate quantum mechanical modeling of large molecular systems using small basis set Hartree–Fock methods corrected with atom-centered potentials. VK Prasad, A Otero-de-la-Roza, GA DiLabio. Journal of Chemical Theory and Computation. 18 (4), 2208-2232. (2022)
- BH9, a new comprehensive benchmark data set for barrier heights and reaction energies: assessment of density functional approximations and basis set incompleteness potentials. VK Prasad, Z Pei, S Edelmann, A Otero-de-la-Roza, GA DiLabio. Journal of Chemical Theory and Computation. 18 (1), 151-166. (2021)
- BSE49, a diverse, high-quality benchmark dataset of separation energies of chemical bonds. VK Prasad, MH Khalilian, A Otero-de-la-Roza, GA DiLabio. Scientific Data. 8 (1), 300. (2021)
- PEPCONF, a diverse data set of peptide conformational energies. VK Prasad, A Otero-de-la-Roza, GA DiLabio. Scientific Data. 6, 180310. (2019)
Are you the profile owner?
Login to edit.