Incorporation of Internal Coordinates Interpolation into the Freezing String Method

We present an improved method for determining guess structures for transition state searches by incorporating internal coordinate interpolation into the freezing string method (FSM). We test our method on over 40 reactions across 3 benchmark data sets covering a diverse set of chemical reactions. Our results show that incorporation of internal coordinate interpolation improves the reliability of the FSM, enabling larger interpolation step sizes and fewer optimization steps per cycle, which together yield nearly a 50% reduction in computational cost while maintaining a 100% success rate on benchmark chemical reaction test cases, including systems where previous attempts based on linear synchronous transit interpolation have failed. We provide an open-source Python implementation of the FSM, in addition to the reactant, product, and transition state structures of all reactions studied.