Doctoral defence in Chemistry - Rohit Goswami

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Doctoral candidate: Rohit Goswami

Title of thesis: Efficient Exploration of Chemical Kinetics -- Development and application of tractable Gaussian Process Models

Opponents: Dr. Sigurður I. Erlingsson, Professor at Reykjavik University, Iceland
Dr. Normand Mousseau, Professor at Université de Montréal, Canada

Advisor: Dr. Hannes Jónsson, Professor at the Faculty of Physical Sciences, University of Iceland

Other members of the doctoral committee:
Dr. Egill Skúlason, Professor at the Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland
Dr. Birgir Hrafnkelsson, Professor at the Faculty of Physical Sciences, University of Iceland
Dr. Morris Riedel, Professor at the Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland
Dr. Thomas Bligaard, Professor at the Department of Energy Conversion and Storage, Technical University of Denmark (DTU), Denmark

Chair of Ceremony: Dr.Benjamín R. Sveinbjornsson, Professor and Vice Head of the Faculty of Physical Sciences, University of Iceland.

Abstract

Spatio-temporal control of chemical systems to tune relative rates of competing reactions has been the goal of chemistry since early alchemy. Today, the estimation of the products and rates of chemical reactions as well as the stability of chemicals and materials are fundamental tasks for the chemical industry. Despite leaps in mathematical modeling, with insightful representations of electronic structure to describe many body quantum systems, and inspite of exascale computing resources, efficient methods for determining reaction rates in large scale simulations has remained out of reach. Direct simulation of atomic dynamics is limited by short timescale and small length scale. Recently, there has been rapid advance in the generation of machine learned potential functions, but they require large data sets as input and are not practical when the task is to quickly screen thousands of chemicals or materials to identify optimal candidates for technological applications. They have, furthermore, been limited so far to regions of stable configurations of the atoms and are not reliable for the transition state regions which are needed for estimating reaction rates. Attempts to explore reaction networks in an automated manner at sufficient accuracy suffer from the large computational cost of the electronic structure calculations. Simplifying approximations for rate calculations recognise that reactions represent slow processes on the time scale of atomic vibrations and thermal equilibration, and make use of statistical approximations for chemical rate calculations. In the simplest approximation, the harmonic approximation to transition state theory, they boil down to finding first order saddle points on the energy surface describing how the system's energy depends on the position of the atoms. Even so, the computational effort in saddle point searches is prohibitively large in many cases especially when the energy and atomic forces are obtained from electronic structure calculations. Surrogate model based acceleration of saddle point searches have been described as promising for almost a decade now, but in practical terms have remained crippled by large computational overhead and numerical instabilities that negate the advantage in wall time. This dissertation presents a solution based on a holistic approach that co-designs the physical representation, statistical model, and systems architecture. This philosophy is embodied in the Optimal Transport Gaussian Process (OT-GP) framework, which uses a physics-aware representation based on optimal transport metrics to create a compact and chemically relevant surrogate of the potential energy surface. This defines a statistically robust approach and uses targeted sampling to reduce the computational effort. Alongside rewrites for the EON software for long timescale simulations, we present a reinforcement-learning approach for the minimum-mode following method when final state is not known and nudged elastic band method when both initial and final state are specified. Collectively, these advances establish a representation-first, service-oriented paradigm for chemical kinetics simulations. The success of this paradigm is demonstrated through large-scale benchmarks where the framework shows state of the art performance characteristics, validated with Bayesian hierarchical models. By delivering a framework for high performance open-source tooling, this work transforms a long-held theoretical promise into a practical engine for exploring chemical kinetics.

About the doctoral candidate

Rohit Goswami was born in Brookhaven, New York, USA. He completed his B.Tech. in Chemical Engineering at Harcourt Butler Technical University in India. After working at the Indian Institute of Technology Kanpur, he began his doctoral studies at the University of Iceland. Under the supervision of Prof. Hannes Jónsson, and with the support of his committee and collaborators, his doctoral research focused on the efficient exploration of chemical kinetics.

His work introduces novel computational representations to create tractable and efficient Gaussian Process models. This approach significantly accelerates the simulation of complex chemical reaction pathways, a fundamental challenge in computational chemistry. Alongside his research, he has actively taken on positions of responsibility within the open-source software community.

He currently works at EPFL in Lausanne, Switzerland.

Rohit extends his sincere gratitude to his family for their unwavering support—especially his wife, Ruhi; his parents, Prof. Debabrata Goswami and Sonaly Goswami; and his sister, Dr. Amrita Goswami, and her husband, Dr. Moritz Sallermann—as well as to his invaluable pets and plants.