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UID:161@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20251128T120000
DTEND;TZID=Asia/Kolkata:20251128T130000
DTSTAMP:20251118T053212Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-colloquium-102-cds-28novembe
 r-2025-chemxdyn-dynamics-aware-methodology-for-chemically-accurate-species
 -identification-and-kinetic-analysis-from-atomistic-simulations/
SUMMARY:Ph.D: Thesis Colloquium: 102 : CDS: 28\,November 2025 "ChemXDyn: Dy
 namics-aware methodology for chemically accurate species identification an
 d kinetic analysis from atomistic simulations"
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nPh.D. Thesis Col
 loquium\n\n\n\nSpeaker: Mr. M B RAJ\nS.R. Number: 06-18-01-10-12-20-1-1848
 6\nTitle: "ChemXDyn: Dynamics-aware methodology for chemically accurate sp
 ecies identification and kinetic analysis from atomistic simulations"\nRes
 earch Supervisor: Dr. Konduri Aditya\, Dr. Phani Motamarri\nDate &amp\; Ti
 me : November 28\, 2025 (Friday)\, 12:00 Noon\nVenue : #102\, CDS Seminar 
 Hall\n\n\n\nABSTRACT\n\nAccurate prediction of ignition\, flame propagatio
 n\, pollutant formation\, and soot inception in advanced combustion system
 s relies on chemical kinetic (CK) models whose reliability is limited by g
 aps in experimental data and the complexity of multiscale reactive process
 es. Reactive molecular dynamics (RMD) simulations offer a powerful bottom-
 up route to uncover atomistic reaction pathways\, but their impact has bee
 n constrained by the lack of robust methods for extracting chemically mean
 ingful species and reactions from noisy\, high-temperature trajectories.\n
 \nThis thesis develops ChemXDyn\, a dynamics-aware framework that integrat
 es temporal interatomic distance analysis\, and valence/coordination consi
 stency to reliably detect bond formation\, dissociation\, and molecular co
 nnectivity in both classical and machine-learned potential–based molecul
 ar dynamics. ChemXDyn overcomes long-standing limitations of frame-by-fram
 e thresholding methods such as ChemTraYzer and ReacNetGenerator\, which fr
 equently misinterpret vibrational fluctuations\, transient encounters\, an
 d π–π stacking as chemical events.\n\nChemXDyn is validated comprehens
 ively across three major combustion systems:\n\n 	Hydrogen oxidation: Chem
 XDyn provides noise-free\, chemically consistent species profiles and accu
 rately identifies true reaction occurrences\, enabling reliable extraction
  of rate constants. These ChemXDyn-derived parameters correct key reaction
 s in the Li et al. mechanism and reproduce ignition delays and laminar fla
 me speeds across multiple temperatures\, significantly outperforming exist
 ing MD analyzers.\n 	Ammonia oxidation: The method captures critical NOx-f
 orming and consuming pathways with high chemical fidelity under different 
 thermochemical and mixing conditions.\n 	Methane oxidation using machine-l
 earned potentials: ChemXDyn reconstructs the canonical CH₄ → CO₂ oxi
 dation sequence\, demonstrating seamless applicability to neural-network M
 D and ab initio–level potentials.\n\nThe thesis further extends ChemXDyn
  to soot-forming ethylene oxidation\, where it distinguishes covalent bond
 ing from π-stacking\, enabling accurate identification of early aromatic 
 nucleation pathways. Additionally\, ChemXDyn is applied to vibrational non
 equilibrium H₂–O₂ systems\, revealing how mode-specific excitation r
 eshapes radical chemistry and dramatically accelerates ignition.\n\nOveral
 l\, this work establishes ChemXDyn as a general\, scalable\, and chemicall
 y rigorous tool for extracting species\, reactions\, and rate parameters f
 rom atomistic simulations. By bridging molecular dynamics with continuum-s
 cale kinetic modelling\, the thesis advances a unified framework for under
 standing combustion\, pollutant formation\, nonequilibrium chemistry\, and
  emerging clean-fuel technologies.\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Ph.D. Thesis Colloquium
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