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  3. Benchmarking The Uma Foundation Interatomic Potential For Gas-phase Chemical Kinetics.
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  3. Benchmarking The Uma Foundation Interatomic Potential For Gas-phase Chemical Kinetics.

Related Experiment Video

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Published on: April 8, 2020

Benchmarking the UMA Foundation Interatomic Potential for Gas-Phase Chemical Kinetics.

Daniel T Kendall1,2, Judit Zádor1

  • 1Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States.

The Journal of Physical Chemistry. A
|June 1, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Foundation models like UMA can accelerate gas-phase chemical kinetics calculations. A hybrid workflow using UMA for exploration and DFT for refinement offers efficient pathway discovery and accurate rate coefficients.

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Published on: April 12, 2019

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Area of Science:

  • Computational Chemistry
  • Chemical Kinetics
  • Machine Learning

Background:

  • Machine-learned interatomic potentials offer rapid access to potential energy surfaces.
  • Their application to gas-phase chemical kinetics, crucial for combustion and atmospheric chemistry, is largely unexplored.

Purpose of the Study:

  • To benchmark the Universal Models for Atoms (UMA) foundation model for gas-phase kinetics.
  • To evaluate UMA's performance in reaction pathway discovery and kinetic data generation using the KinBot workflow.

Main Methods:

  • Benchmarking UMA across 12 diverse gas-phase systems using the automated KinBot workflow.
  • Comparing UMA-optimized structures and energies against high-level ab initio theory (ωB97M-V/def2-TZVPD).
  • Assessing stationary-point fidelity, conformer ordering, and hindered rotor scans.

Main Results:

  • UMA reliably identifies relevant reaction channels, even those challenging for traditional ab initio methods.
  • A hybrid workflow combining UMA exploration with DFT refinement provides efficient and accurate energy calculations.
  • UMA significantly accelerates the computation of rate coefficients for gas-phase systems.

Conclusions:

  • UMA shows strong potential for accelerating gas-phase kinetics studies.
  • A hybrid UMA-DFT workflow is practical for exploring potential energy surfaces and refining key kinetic parameters.
  • Future work should focus on uncertainty quantification and specialized fine-tuning for kinetics applications.