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ChemXDyn: Dynamics-informed species and reaction detection methodology from atomistic simulations

Raj Maddipati, Dhruthi Boddapati, Elangannan Arunan, Phani Motamarri, Konduri Aditya

TL;DR

ChemXDyn is introduced, a dynamics-aware computational methodology that leverages time-resolved interatomic distance signatures as a core principle to robustly identify chemically consistent bonded interactions and, consequently, extract meaningful reaction pathways.

Abstract

Accurate identification of chemical species and reaction pathways from molecular dynamics (MD) trajectories is a prerequisite for deriving predictive chemical-kinetic models and for mechanistic discovery in reactive systems. However, state-of-the-art trajectory analysis methods infer bonding from instantaneous distance thresholds, which can misclassify transient, nonreactive encounters as bonds and thereby introduce spurious intermediates, distorted reaction networks, and biased rate estimates. Here, we introduce ChemXDyn, a dynamics-aware computational methodology that leverages time-resolved interatomic distance signatures as a core principle to robustly identify chemically consistent bonded interactions and, consequently, extract meaningful reaction pathways. In particular, ChemXDyn propagates molecular connectivity through time while enforcing atomic valence and coordination constraints to distinguish genuine bond-breaking and bond-forming events from transient, nonreactive encounters. We evaluate ChemXDyn on ReaxFF MD simulations of hydrogen and ammonia oxidation and on neural-network potential MD simulations of methane oxidation, and benchmark its performance against widely used trajectory analysis methods. Across these cases, ChemXDyn suppresses unphysical species prevalent in static analyses, recovers experimentally consistent reaction pathways, and improves the fidelity of rate constant estimation. In ammonia oxidation, ChemXDyn removes unphysical intermediates and resolves key NOx- and N2O-forming and -consuming routes. In methane oxidation, it reconstructs the canonical progression from CH4 to CO2. By linking atomistic dynamics to chemically consistent reaction identification, ChemXDyn provides a transferable foundation for MD-derived reaction networks and kinetics, with potential utility spanning combustion, catalysis, plasma chemistry, and electrochemical environments.

ChemXDyn: Dynamics-informed species and reaction detection methodology from atomistic simulations

TL;DR

ChemXDyn is introduced, a dynamics-aware computational methodology that leverages time-resolved interatomic distance signatures as a core principle to robustly identify chemically consistent bonded interactions and, consequently, extract meaningful reaction pathways.

Abstract

Accurate identification of chemical species and reaction pathways from molecular dynamics (MD) trajectories is a prerequisite for deriving predictive chemical-kinetic models and for mechanistic discovery in reactive systems. However, state-of-the-art trajectory analysis methods infer bonding from instantaneous distance thresholds, which can misclassify transient, nonreactive encounters as bonds and thereby introduce spurious intermediates, distorted reaction networks, and biased rate estimates. Here, we introduce ChemXDyn, a dynamics-aware computational methodology that leverages time-resolved interatomic distance signatures as a core principle to robustly identify chemically consistent bonded interactions and, consequently, extract meaningful reaction pathways. In particular, ChemXDyn propagates molecular connectivity through time while enforcing atomic valence and coordination constraints to distinguish genuine bond-breaking and bond-forming events from transient, nonreactive encounters. We evaluate ChemXDyn on ReaxFF MD simulations of hydrogen and ammonia oxidation and on neural-network potential MD simulations of methane oxidation, and benchmark its performance against widely used trajectory analysis methods. Across these cases, ChemXDyn suppresses unphysical species prevalent in static analyses, recovers experimentally consistent reaction pathways, and improves the fidelity of rate constant estimation. In ammonia oxidation, ChemXDyn removes unphysical intermediates and resolves key NOx- and N2O-forming and -consuming routes. In methane oxidation, it reconstructs the canonical progression from CH4 to CO2. By linking atomistic dynamics to chemically consistent reaction identification, ChemXDyn provides a transferable foundation for MD-derived reaction networks and kinetics, with potential utility spanning combustion, catalysis, plasma chemistry, and electrochemical environments.
Paper Structure (4 sections, 2 equations, 13 figures, 3 tables)

This paper contains 4 sections, 2 equations, 13 figures, 3 tables.

Table of Contents

  1. Determination of equilibrium bond lengths and bond orders from IAD/BO distributions
  2. Sensitivity of single-bond threshold and window size parameters
  3. Weak interaction threshold
  4. Window size

Figures (13)

  • Figure 1: ChemXDyn workflow for extracting reactions and rate constants from MD trajectories. Step A: Two successive MD snapshots (R1 and R2) are analyzed, with bond order (BO) trajectories plotted for four representative bonds. Each trajectory plot includes backward (blue) and forward (red) time-window averages of BO(t), allowing us to distinguish between stable bonds and transient events associated with bond formation, breakage, or transition states. Step B: Temporally averaged BOs from Step A are compared with equilibrium BO (or IAD) reference values obtained from peaks of BO distribution plots to classify bonds as single, double, etc., while discarding weak, non-bonded interactions. Step C: The validated bonds and their assigned multiplicities are used to construct a connectivity graph, and a depth-first search (DFS) algorithm groups atoms into molecular species at each timestep. Step D: Species from consecutive timesteps are compared to detect reactions and further these reaction counts ($\ce{N_{reac}}$) are accumulated across the trajectory to evaluate rate constants as a function of temperature for different systems.
  • Figure 2: Species and reaction detection in $\ce{H2}/\ce{O2}$ oxidation. (A) Comparison of (i) species occurrences and (ii) average lifetimes obtained from different trajectory analysis methods. (B) Temporal evolution of $\ce{O2}$ for (i) ReaxFF MD and (ii) NNMD trajectories, highlighting differences between methods. (C) (i) Total number of reactions detected by each method across temperatures. (ii) Cumulative counts of those reactions present in Li et al. mechanism li across temperatures obtained using CTY-F, CTY-V and ChemXDyn.
  • Figure 3: Temporal bond order (BO) evolution for different atom-pairs involved in two distinct collision events. (i) A genuine reaction event where the $H^{(1)}$–$H^{(2)}$ bond breaks and an $O$–$H^{(1)}$ bond forms, captured by the deviation of both forward (red) and backward (blue) averaged BOs. (ii) A transient, nonreactive collision between $\ce{H2}$ and $\ce{O2}$ showing no sustained deviation in averaged BOs. A video of this collision event is available in (see SI video V1).
  • Figure 4: Rate constants ($k$) for individual reactions obtained from NVT simulations at four temperatures ($T$) using CTY-F, CTY-V and ChemXDyn, compared with the Arrhenius fit obtained from the Li et al. mechanism li.
  • Figure 5: Time evolution of key species in the $\ce{NH3}/\ce{N2}/\ce{O2}$ oxidation system extracted using CTY-F (representative of CTY-V as well) and ChemXDyn.
  • ...and 8 more figures