What is it about?
A fundamental task in chemistry is finding the "transition state"—the highest energy point along a reaction pathway, like the peak of a mountain pass. Finding this point accurately with computer simulations is crucial but incredibly slow, as each step in the search requires a very expensive electronic structure calculation. In this work, we dramatically accelerated this search process using a machine learning technique called Gaussian Process Regression (GPR). Our method works by building a cheap, approximate "surrogate" map of the energy landscape on the fly. After just a few expensive calculations, the GPR model learns the local terrain. The search algorithm can then take many fast, intelligent steps on this cheap map before needing another "real" calculation to confirm its position. This "GPR-dimer" method reduces the number of expensive calculations needed by an order of magnitude—turning a search that might have taken 300 steps into one that takes only 30.
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Why is it important?
This work provides a massive speedup for a foundational task in computational chemistry, enabling researchers to study more complex reactions or screen large numbers of potential reactions much faster than before. We demonstrated that our "smart" search algorithm, using simple Cartesian coordinates, performs just as well as (and sometimes better than) other highly complex methods that rely on sophisticated internal coordinates. Crucially, we provided a new, highly efficient C++ implementation of this method. This makes the GPR acceleration practical enough to reduce the total wall-clock time of a simulation, not just the number of steps, providing a direct and tangible benefit to the scientific community.
Perspectives
This was technically one of my first projects when I started my PhD, and it was a fantastic way to dive in. I had the opportunity to collaborate with experts from SURFsara, the Dutch national supercomputing center, to take a powerful idea and re-engineer it into a truly high-performance tool. We rewrote the GPR-acceleration algorithm in C++ and then demonstrated its power on a large, challenging benchmark set of 500 chemical reactions. The results were incredibly rewarding. Seeing an order-of-magnitude reduction in computational cost is a huge win in this field. But I was even more excited to show that our 'simpler' Cartesian coordinate approach, when boosted with a smart ML model, could go toe-to-toe with more complex, state-of-the-art methods. Getting this work published in a top journal like JCTC was a great start to my PhD. It validated our approach and showed that the biggest gains often come from the intelligent fusion of physics-based algorithms, machine learning, and high-performance software engineering.
Rohit Goswami
University of Iceland
Read the Original
This page is a summary of: Efficient Implementation of Gaussian Process Regression Accelerated Saddle Point Searches with Application to Molecular Reactions, Journal of Chemical Theory and Computation, August 2025, American Chemical Society (ACS),
DOI: 10.1021/acs.jctc.5c00866.
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