@article{goswamiTwodimensionalRMSDProjections2026,
title = {Two-Dimensional {{RMSD}} Projections for Reaction Path Visualization and Validation},
author = {Goswami, Rohit},
year = {2026},
month = {mar},
journal = {MethodsX},
pages = {103851},
issn = {2215-0161},
doi = {10.1016/j.mex.2026.103851},
urldate = {2026-03-06},
langid = {english},
keywords = {journal, selected},
}
Rohit Goswami
Postdoctoral Researcher · EPFL labCOSMO
Better computational representations unlock better science. I develop surrogate models, cross-language interfaces, and ML infrastructure that make chemical kinetics simulations practical at scale.
My work connects transition state theory, machine learning interoperability, and scientific software engineering through a common principle: the right computational representation unlocks science that was previously out of reach. From relativistic atomic structure to lava flow prediction, I develop the abstractions, implement them in open-source software, and validate them on real chemical systems.
Research Threads
transition-state
Transition State Methods
NEB, dimer methods, and path visualization for reaction pathways
bayesian-stats
Bayesian Statistical Methods
Hierarchical models and uncertainty quantification for algorithm benchmarking
gp-acceleration
GP-Accelerated Saddle Point Searches
Surrogate energy surfaces via Gaussian process regression for 10x fewer force evaluations
sci-sw-interop
Scientific Software Interoperability
Cross-language bindings, build systems, and packaging for computational science codes
ml-atomistic
Machine Learning Interoperability for Molecular Science
Foundational libraries enabling ML models and simulation engines to communicate
molecular-sim
Molecular Simulation and Structural Analysis
Ice nucleation, self-propelled particles, lava flows, and tools for trajectory analysis
reproducible-hpc
Reproducible Scientific Computing
Nix, pixi, spack, and workflow tools for HPC environments
ultrafast
Ultrafast Spectroscopy and Pulse Shaping
Femtosecond lasers for spectroscopy, quantum gates, and machine-learned pulse optimization
Recent Work
@article{goswamiAdaptivePruningOA2025,
title = {Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches},
author = {Goswami, Rohit and J{\'o}nsson, Hannes},
journal = {ChemPhysChem (Cover feature)},
date = {2025-11-27},
doi = {10.1002/cphc.202500730},
copyright = {\copyright{} 2025 The Author(s). ChemPhysChem published by Wiley-VCH GmbH},
langid = {english},
archivePrefix = {arXiv},
eprint = {2510.06030},
eprinttype = {arxiv},
keywords = {journal, selected},
}@article{sallermannFlowyHighPerformance2025,
title = {Flowy: High Performance Probabilistic Lava Emplacement Prediction},
shorttitle = {Flowy},
author = {Sallermann, Moritz and Goswami, Amrita and {Pe{\~n}a-Torres}, Alejandro and Goswami, Rohit},
year = {2025},
month = {oct},
journal = {Computer Physics Communications},
volume = {315},
pages = {109745},
issn = {0010-4655},
doi = {10.1016/j.cpc.2025.109745},
urldate = {2025-08-03},
langid = {english},
archivePrefix = {arXiv},
eprint = {2405.20144},
eprinttype = {arxiv},
keywords = {journal, selected},
}@article{goswamiBayesianHierarchicalModels2025,
title = {Bayesian Hierarchical Models for Quantitative Estimates for Performance Metrics Applied to Saddle Search Algorithms},
author = {Goswami, Rohit},
journal = {AIP Advances},
volume = {15},
number = {8},
pages = {85210},
issn = {2158-3226},
doi = {10.1063/5.0283639},
date = {2025-08-11},
langid = {english},
archivePrefix = {arXiv},
eprint = {2505.13621},
eprinttype = {arxiv},
keywords = {journal, selected},
}