Adapting Signal Recovery Algorithms for Accurate and Efficient Reaction Rate Calculations

Shaama Mallikarjun Sharada, University of Souther California

Chemical reactions lie at the heart of processes designed to meet our growing energy and material needs. The first step towards designing and optimizing chemical reactions involves identification of underlying mechanisms and quantification of rates. Quantum chemistry methods along with theories such as transition state theory (TST) are indispensable for this purpose and have played a pivotal role in elucidating mechanisms in recent decades. While widely successful, conventional TST is relatively simplistic and can lead to inaccurate rates for many classes of reactions. Alternative, more accurate rate theories such as variational transition state theory (VTST) are well-established but incur exceptionally high computational costs which limits their widespread use. Our goal is to lower these costs, thereby enhancing the reliability of rate predictions, by adapting algorithms typically used in signal processing and information recovery. Matrix completion methods are widely used to recover signals from noisy, incomplete data with high fidelity. Matrix completion has been previously used to solve the Netflix problem: determining recommendations for films based on incomplete information of a user’s preferences by exploiting information about the preferences of others with similar tastes. We demonstrate that these methods can be employed as cost-reduction strategies, to recover otherwise expensive second derivatives of energy for points on the minimum energy path (MEP) of a reaction. The algorithm, termed harmonic variety-based matrix completion (HVMC), utilizes the underlying polynomial structure of potential energies of points on the MEP, and construct a low-rank problem. A small fraction of the elements are computed (sampled) using finite differences of gradients, available at low-cost, and HVMC completes the missing information by exploiting our proposed matrix recovery methods. Based on the accuracy of resulting rate predictions for model reactions, we aim to leverage additional problem features to further enhance algorithm efficiency.

Bio: Shaama Mallikarjun Sharada’s research interests span the development of algorithms to enhance the reliability of quantum chemistry-based reaction rate predictions and their application towards the design of viable catalysts for sustainable chemistry transformations. Dr. Sharada received her Bachelors and Masters in Chemical Engineering from the Indian Institute of Technology, Bombay (India) where she was awarded the Institute Gold Medal. She received her PhD in Chemical Engineering from UC Berkeley in 2015 for her work on the development of efficient reaction path search and wavefunction stability algorithms for catalysis applications. As a postdoctoral researcher at Stanford University, her work spanned the development of machine learning density functionals, construction of databases for benchmarking functional accuracies for surface chemistry, and design of electrocatalytic systems for ammonia synthesis. Since 2017, Prof. Sharada is the WiSE Gabilan Assistant Professor in the Mork Family Department of Chemical Engineering and Materials Science and Assistant Professor (by Courtesy) in the Department of Chemistry at the University of Southern California. She is a recipient of the 2020 ACS Petroleum Research Fund Doctoral New Investigator Award and is a 2020 Scialog Fellow for the Negative Emissions Science initiative.

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