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UID:226@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20260918T100000
DTEND;TZID=Asia/Kolkata:20260918T110000
DTSTAMP:20260909T144502Z
URL:https://cds.iisc.ac.in/events/ph-d-thesis-defense-102-cds-18-september
 -2026-chemxdyn-dynamics-aware-methodology-for-chemically-accurate-species-
 identification-and-kinetic-analysis-from-atomistic-simulations/
SUMMARY:Ph.D: Thesis Defense: 102: CDS: 18\, September 2026 "ChemXDyn: Dyna
 mics-aware methodology for chemically accurate species identification and 
 kinetic analysis from atomistic simulations"
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nPh.D. Thesis Def
 ense\n\n\n\nSpeaker: Mr. M B RAJ\nS.R. Number: 06-18-01-10-12-20-1-18486\n
 Title: "ChemXDyn: Dynamics-aware methodology for chemically accurate speci
 es identification and kinetic analysis from atomistic simulations"\nResear
 ch Supervisor: Dr. Konduri Aditya\, Dr. Phani Motamarri\nDate &amp\; Time 
 : September 18\, 2026 (Friday)\, 10:00 AM\nVenue : #102\, CDS Seminar Hall
 \n\n\n\nABSTRACT\n\nAccurate prediction of ignition\, flame propagation\, 
 pollutant formation\, and soot inception in advanced combustion systems re
 lies on chemical kinetic (CK) models whose reliability is limited by gaps 
 in experimental data and the complexity of multiscale reactive processes. 
 Reactive molecular dynamics (RMD) simulations offer a powerful bottom-up r
 oute to uncover atomistic reaction pathways\, but their impact has been co
 nstrained by the lack of robust methods for extracting chemically meaningf
 ul species and reactions from noisy\, high-temperature trajectories.\n\nTh
 is thesis develops ChemXDyn\, a dynamics-aware framework that integrates t
 emporal interatomic distance analysis\, and valence/coordination consisten
 cy to reliably detect bond formation\, dissociation\, and molecular connec
 tivity in both classical and machine-learned potential–based molecular d
 ynamics. ChemXDyn overcomes long-standing limitations of frame-by-frame th
 resholding methods such as ChemTraYzer and ReacNetGenerator\, which freque
 ntly misinterpret vibrational fluctuations\, transient encounters\, and π
 –π stacking as chemical events.\n\nChemXDyn is validated comprehensivel
 y across three major combustion systems:\n\n 	Hydrogen oxidation: ChemXDyn
  provides noise-free\, chemically consistent species profiles and accurate
 ly identifies true reaction occurrences\, enabling reliable extraction of 
 rate constants. These ChemXDyn-derived parameters correct key reactions in
  the Li et al. mechanism and reproduce ignition delays and laminar flame s
 peeds across multiple temperatures\, significantly outperforming existing 
 MD analyzers.\n 	Ammonia oxidation: The method captures critical NOx-formi
 ng and consuming pathways with high chemical fidelity under different ther
 mochemical and mixing conditions.\n 	Methane oxidation using machine-learn
 ed potentials: ChemXDyn reconstructs the canonical CH₄ → CO₂ oxidati
 on sequence\, demonstrating seamless applicability to neural-network MD an
 d ab initio–level potentials.\n\nThe thesis further extends ChemXDyn to 
 soot-forming ethylene oxidation\, where it distinguishes covalent bonding 
 from π-stacking\, enabling accurate identification of early aromatic nucl
 eation pathways. Additionally\, ChemXDyn is applied to vibrational nonequi
 librium H₂–O₂ systems\, revealing how mode-specific excitation resha
 pes radical chemistry and dramatically accelerates ignition.\n\nOverall\, 
 this work establishes ChemXDyn as a general\, scalable\, and chemically ri
 gorous tool for extracting species\, reactions\, and rate parameters from 
 atomistic simulations. By bridging molecular dynamics with continuum-scale
  kinetic modelling\, the thesis advances a unified framework for understan
 ding combustion\, pollutant formation\, nonequilibrium chemistry\, and eme
 rging clean-fuel technologies.\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Thesis Defense
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