Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning

Anbu Selvam Venkatachalam; Artem Rudenko; Daniel Rolles; Huynh Van Sa Lam; Joshua Stallbaumer; Loren Greenman

REVIEW 3 major objections 3 minor 1 cited by

Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning

T0 review · 3 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper shows that complete six- and eight-ion Coulomb explosion detection, analyzed by machine learning, distinguishes molecular isomers on an event-by-event basis.

desk verdict Complete six- and eight-fold coincidence CEI with simulation-trained ML separation is a genuine advance; the transfer claim is credible but the simulation's fitted parameters leave model-form error untested. read the letter →

arxiv 2509.03776 v1 pith:FDBA56VK submitted 2025-09-03 physics.chem-ph physics.comp-ph

Anbu Selvam Venkatachalam , Loren Greenman , Joshua Stallbaumer , Artem Rudenko , Daniel Rolles , Huynh Van Sa Lam This is my paper

classification physics.chem-phphysics.comp-ph

keywords Coulombexplosionimagingmulti-coincidencedetectionmachinelearningmolecularisomeridentificationmomentum-spacecorrelationsUMAPdimensionalityreductiondichloroethyleneisomersisoxazolefragmentation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that 'complete' Coulomb explosion imaging—recording every atomic ion produced when a laser shatters a molecule—can be extended to intermediate-sized molecules, and that the resulting high-dimensional momentum patterns contain enough correlated information to identify molecular geometry automatically. It demonstrates six-ion coincidence detection for cis- and trans-1,2-dichloroethylene and an eight-ion channel for isoxazole, with momentum conservation making the data background-free. On a mixed experimental sample, unsupervised dimensionality reduction and density-based clustering separate the two isomers event by event without labels; supervised feature analysis then shows that momentum-angle correlations, especially the Cl+–Cl+ angle, carry most of the structural signal. The central test is a supervised embedding trained purely on classical Coulomb explosion simulations and projected onto real experimental events: HDBSCAN recovers about 99% of trans and 84% of cis events, with an overall misclassification rate of 5.5%. If the claim holds, complete CEI plus machine learning provides an automated, scalable route to imaging molecular structure and disentangling weak reaction channels and minority species at the single-molecule level.

What carries the argument

The central object is the multi-coincidence CEI event: one laser shot that fully atomizes a molecule into detected ionic fragments whose 3D momenta are recorded together; momentum conservation makes such events background-free. The machinery is UMAP (nonlinear dimensionality reduction that embeds high-dimensional momentum vectors into a low-dimensional space), HDBSCAN (density-based clustering that needs no preset cluster count), and Random Forest feature importance, which ranks momentum components, inter-fragment distances d_ij, angles θ_ij, and plane angles ϕ_ijkl by discriminative power. The labeled training data come from a classical point-charge Coulomb explosion simulation with Gaussia

What would settle it

Prepare a known cis/trans mixture of 1,2-DCE at a pump–probe delay where a transient twisted geometry is populated, train supervised UMAP on point-charge simulations of cis, trans, twisted, and 1,1-DCE, and compare HDBSCAN-assigned branch fractions with an independently measured transient population, e.g., from time-resolved spectroscopy. An assignment error beyond the reported ~5.5% at those delays, or a shift of experimental events out of their simulated clusters when the training model is changed to include sequential fragmentation, would falsify the transferability claim.

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Extended reading notes

Core claim

The paper claims that complete Coulomb explosion imaging—recording all atomic ions from a single laser shot—works for intermediate-sized molecules, and that machine learning can extract geometry from the high-dimensional momentum correlations. It shows six-ion coincidences for dichloroethylene isomers and an eight-ion channel for isoxazole, with momentum conservation providing background-free data; unsupervised UMAP plus HDBSCAN automatically separates mixed experimental events. The key validation is a supervised UMAP embedding trained only on simulated point-charge explosions that transfers to real data, recovering ~99% of trans and ~84% of cis events with 5.5% misclassification; the author

Load-bearing premise

The pipeline's transfer to real data assumes the classical point-charge Coulomb explosion simulation, with empirically fitted spatial spread and kinetic energy, reproduces the angular correlations of real explosions closely enough; if ionization dynamics, multiple charge states, or sequential fragmentation distort those correlations, the simulation-trained embedding could separate simulated geometries while misassigning experimental events.

Editorial extensions

If this is right

Complete six- and eight-ion coincidence CEI is feasible with tabletop lasers and standard detectors, extending complete all-atom imaging from five-atom systems to intermediate-sized molecules.
Because complete coincidence data are background-free, weak channels and minority species such as dimers can be identified without manual gating on specific projections.
Unsupervised UMAP plus HDBSCAN can separate coexisting isomers from a single mixed sample and yield clean momentum images for each structure.
A supervised UMAP embedding trained only on simulated data transfers to experimental events, achieving ~99% trans and ~84% cis recovery with 5.5% overall misclassification in the demonstrated case.
Feature-importance analysis identifies which momentum observables distinguish structures—angles between fragment momenta and between momentum planes—so future experiments know which correlations to record.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the simulation-to-experiment transfer holds generally, the same pipeline could classify conformers, enantiomers, and transient photochemical geometries in pump–probe measurements, where ground-truth labels do not exist; the paper sets up but does not demonstrate this.
The dominance of angle correlations over momentum magnitudes suggests that angular acceptance of the detector may matter more than absolute momentum calibration; a testable design implication for future CEI spectrometers.
Because the paper reports that incomplete channels have not yet been classified, a natural extension is to impute missing fragments and quantify how much structural information survives partial detection.
The transferability claim could be stress-tested by generating training labels from a fragmentation model that includes sequential decay or multiple charge states rather than instantaneous point-charge ionization; if experimental events still land inside the correct clusters, the method is insensitive to that model-form choice.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: this paper delivers something real, kinematically complete Coulomb explosion imaging with up to eight detected ions on a tabletop laser, plus a machine-learning pipeline that assigns molecular structure event-by-event. The experimental cis/trans separation is solid; the simulation-to-experiment transfer is the load-bearing part and it mostly holds up, but the validation has a gap worth naming.

What's new: previous work from this group used partial four-fold coincidence on isoxazole; here they demonstrate complete six-fold DCE and eight-fold isoxazole detection, and they automate isomer identification with UMAP + HDBSCAN and Random Forest feature ranking. The feature analysis is a real strength: they show angle correlations between fragment momenta, not absolute momenta, carry the discrimination, which is consistent with the simulation overestimating momentum magnitudes (SI Fig. S6) while still reproducing angular structure. The comparison of UMAP with PCA/t-SNE, the stability checks over random states, and the parameter sweeps in SI Sec. IV C are careful. Simulation code is on Zenodo and source data are provided, which makes the work reproducible rather than hand-wavy.

The soft spot is exactly where the stress-test points. The simulation's two free parameters (0.25 A spatial spread and 500 meV kinetic energy) are fit to experimental momentum widths, so agreement on marginal distributions is partly definitional. More importantly, the supervised UMAP is trained on a point-charge, instantaneous-ionization Coulomb model, and the robustness checks vary only those two fitted parameters. That cannot detect errors in model form: sequential fragmentation, finite ionization time, or charge-state mixtures could in principle distort joint angle correlations in a class-dependent way. The paper's defense, that angle correlations are the discriminators and that the simulation reproduces the Cl-Cl angle well, is reasonable and goes some way, but it does not directly certify the joint multi-ion correlations the embedding exploits. I would call this a moderate caveat, not a fatal flaw. The experimental transfer in Fig. 6(c) shows clear overlap, and the misclassification rate is low. For a revision, I would ask for explicit background/false-coincidence rejection rates after momentum-conservation filtering, event counts and statistics for the eight-fold isoxazole channel, and a direct test of the transfer using a simulation with a different model form, e.g., including sequential fragmentation or a charge-state distribution, to see if the classification changes materially.

The paper is aimed at the ultrafast molecular imaging and coincidence spectroscopy community. It deserves a serious referee; the claims are concrete, falsifiable, and the experimental work is substantial. I would send it for peer review with requests for those clarifications.

Referee Report

3 major / 3 minor

Summary. The paper presents a complete-coincidence Coulomb explosion imaging (CEI) approach for intermediate-sized molecules, demonstrating six-fold coincidences for dichloroethylene and eight-fold coincidences for isoxazole using a tabletop 3-kHz laser. It combines UMAP dimensionality reduction with HDBSCAN clustering to separate cis/trans isomers from an experimental mixture on an event-by-event basis, and uses Random Forest feature importance to identify the most discriminating momentum-space observables, notably angle correlations such as θ56 and θ34. The authors further train a supervised UMAP embedding on classical point-charge Coulomb explosion simulations of four molecular geometries and claim that this simulation-trained classifier transfers to experimental data, recovering 99% of trans and 84% of cis events in Fig. 6(d). The central claims are that complete CEI of up to eight ions is feasible and that ML can exploit high-dimensional momentum correlations for automated molecular structure identification.

Significance. If the claims hold, this is a useful methodological advance: it extends complete CEI to eight-atom systems with a tabletop laser and provides a scalable, open-source ML pipeline for event-by-event structure assignment. The experimental demonstration of unsupervised cis/trans separation, validated by comparison with pure-isomer Newton plots, is solid and does not rely on simulation. The paper also ships the Coulomb explosion simulation code and uses standard open-source ML packages, which aids reproducibility. The main weakness is the simulation-to-experiment transfer claim: it is validated for only two isomers, and the simulation is validated by marginal angular distributions plus two fitted broadening parameters. The joint multi-ion correlations exploited by the supervised UMAP embedding are not directly certified, and the robustness tests in SI Sec. IV C vary only the fitted parameters, not the model form. This makes the four-geometry transfer and the phrase 'our model can generalize well to real data' stronger than the evidence supports.

major comments (3)

[Fig. 6 / Methods: Coulomb explosion simulation / SI Sec. IV C] The transfer claim in Fig. 6(c,d) is load-bearing: a supervised UMAP trained on point-charge simulations assigns experimental events. The evidence that the simulation reproduces the relevant joint correlations is indirect: Figs. 1c and 2c compare only marginal azimuthal distributions, and the two simulation parameters (0.25 Å, 500 meV) are fitted to experimental momentum widths. SI Sec. IV C sweeps these parameters over physical ranges, but this does not test model-form errors (e.g., charge-state mixtures, finite ionization time, sequential fragmentation, non-point-charge effects). If such omitted dynamics distort class-dependent joint correlations, the simulated latent space could separate simulated geometries while misassigning real events. The experimental cis/trans transfer is encouraging, but I recommend either adding a model-form robustness check (e.g., charge-state variation or co
[Methods: Coulomb explosion simulation / SI Fig. S6] The simulation parameters are chosen empirically to reproduce the widths of experimental momentum distributions, and SI Fig. S6 shows that the simulation systematically overestimates absolute fragment momenta. The main text's statement that the simulation 'successfully reproduces key experimental features' and 'validates the ability of the simulation to model the Coulomb explosion dynamics with high fidelity' is therefore partly definitional for the widths; the genuinely meaningful agreement is in the angular correlations. Since the supervised UMAP in Fig. 6 is trained on raw momentum components rather than scale-invariant internal coordinates, the systematic scale mismatch could bias the embedding. Please clarify why the absolute-momentum overestimation does not affect the transfer, or train/validate on rotation- and scale-invariant features such as dij and θij, which the paper itself i
[Results, Fig. 5 and Fig. 6] The four-geometry analysis (cis-, trans-, twisted-1,2-DCE, 1,1-DCE) is entirely simulation-based for the twisted and 1,1-DCE geometries, as the paper acknowledges. However, the conclusions—especially the statement that the model 'can generalize well to real data' and the discussion of monitoring complex dynamical transformations—extend beyond the experimentally validated cis/trans system. The experimental validation in Fig. 6(c,d) covers only cis and trans, for which independent ground-truth labels are available. Please state explicitly in the conclusions that the four-geometry classification is a simulation-based demonstration, and that experimental validation for twisted/1,1-DCE is an open question. This would make the scope of the claim commensurate with the evidence.

minor comments (3)

[Methods: Machine-learning-based analysis] Please report the UMAP hyperparameters (n_neighbors, min_dist, metric) and HDBSCAN parameters, and describe whether features were standardized or preprocessed before dimensionality reduction. The code availability statement is helpful, but explicit parameter values are needed for a self-contained reproducibility assessment.
[Fig. 6(d)] The ground-truth labels in Fig. 6(d) are said to be 'derived independently'; please specify how they were obtained and how the 5.5% overall misclassification rate is computed (per-event, weighted by class, or averaged across UMAP restarts). The error-bar procedure is mentioned, but the exact number of restarts and the clustering protocol should be in the Methods or SI.
[SI Sec. I.A] Minor typos: '80-nm diameter' should read '80-mm diameter'; '1 −10 mbar' appears to be a typesetting artifact for '1 × 10^−10 mbar' or similar. The title header also contains a spacing artifact ('fo r').

Circularity Check

1 steps flagged · score 2.0 of 10

Minor fitted-parameter tautology in the width comparison; central isomer-discrimination and simulation-to-experiment transfer claims are not circular.

fitted input called prediction [Methods, 'Coulomb explosion simulation' and Figs. 1/2]
"These parameters were chosen empirically to closely reproduce the widths of the experimentally observed momentum distributions (as shown in Fig. 1 and Fig. 2). ... This model successfully reproduces key features of the experimental momentum distributions, capturing the separation and localization of the fragment ions with good accuracy."

The two simulation parameters (0.25 Å spatial deviation, 500 meV total kinetic energy) are explicitly fitted to match the experimental momentum widths, so the statement that the simulation reproduces those widths is true by construction and does not independently validate the model's widths. The agreement in azimuthal angle distributions (Figs. 1c/2c) is a separate, non-fitted prediction, and the central structure-discrimination results rely on inter-fragment angle correlations rather than on the fitted widths. Thus this is a minor, localized circularity in the validation language, not in the main claim.

full rationale

The paper's core results—unsupervised separation of cis/trans experimental events (Fig. 3), feature-importance analysis (Fig. 4), four-geometry simulation study (Fig. 5), and simulation-trained supervised UMAP applied to experimental data (Fig. 6)—do not reduce to the fitted inputs. The fitted parameters control only the widths of momentum distributions; the discriminative features used for isomer separation are geometry-driven angle correlations (θij, ϕijkl) that are not determined by these two parameters. The simulation model is described in detail rather than merely imported from self-citations, and the robustness tests in SI Sec. IV C vary the fitted parameters without changing the conclusions. The only circular element is the claim that the simulation 'reproduces' the experimental momentum widths after those widths were used to choose the parameters; this is a minor self-consistency check, not a load-bearing prediction.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

The central pipeline depends on two empirically fitted simulation parameters and a simplified point-charge explosion model. No new physical entities are introduced.

free parameters (3)

Gaussian spatial deviation (initial geometry spread) = 0.25 Å (0.5 Å for twisted/1,1-DCE in some scenarios)
Chosen empirically to reproduce experimental momentum distribution widths (Methods, Coulomb explosion simulation).
Total initial kinetic energy = 500 meV (3 eV or 6 eV in some scenarios)
Randomly partitioned among atoms; fitted to experimental distribution widths (Methods).
UMAP hyperparameters (n_neighbors, min_dist) = not specified in main text
UMAP projection quality depends on these; stated only via code/software versions, not in the text.

assumptions (6)

domain assumption Classical point-charge Coulomb explosion model reproduces key experimental features
Used throughout; the paper argues Coulomb repulsion dominates over chemical bonds at high charge states (Methods).
domain assumption Instantaneous vertical ionization to the final charge states
Simulation assumes each atom obtains its final charge at ionization (Methods).
domain assumption The selected complete-coincidence channel (e.g., all singly charged ions) is a faithful readout of the neutral molecular geometry
The paper's experimental comparisons rest on this; incomplete channels are noted to be messier (SI Sec. II).
domain assumption B3LYP/aug-cc-pVDZ optimized ground-state geometries are the relevant initial structures
Used to define cis/trans/twisted/1,1 geometries (SI Sec. III A).
domain assumption Momentum-conservation filtering eliminates all false coincidences
Filtering rejects multi-molecule events but residual backgrounds are not quantified (Methods; the paper calls data background-free).
standard math UMAP/HDBSCAN/Random Forest produce stable, representative structure of the high-dimensional momentum data
Stability checked over 100 runs (SI Sec. IV B); standard open-source implementations used.

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Cite this review

Pith. "Pith review of Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning." pith.science (2026). https://pith.science/paper/FDBA56VK

@misc{pith2026250903776,
  author       = {Pith},
  title        = {Pith review of: Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FDBA56VK}},
  note         = {Machine review of arXiv:2509.03776}
}

read the original abstract

Coulomb explosion imaging (CEI) is a powerful technique for capturing the real-time motion of individual atoms during ultrafast photochemical reactions. CEI generates high-dimensional data with naturally embedded correlations that allow mapping the coordinated motion of nuclei in molecules. This enables reliable separation of competing reaction pathways and makes this approach uniquely suited for characterizing weak reaction channels. However, rich information contained in experimental CEI patterns remains largely underexploited due to challenges in visualizing correlations between multiple observables in multi-dimensional parameter space. Here we present a new approach to CEI of intermediate-sized polyatomic molecules, detecting up to eight ionic fragments in coincidence and leveraging machine-learning-based analysis to identify patterns and correlations in the resulting high-dimensional momentum-space data, enabling robust molecular structure identification and differentiation. Our approach provides high-dimensional background-free data encoding exceptionally rich structural information and establishes an automated, scalable framework for extracting insightful information from the data. As a demonstration, we apply this method to image and distinguish dichloroethylene isomers, showcasing its potential for broader applications in molecular imaging. Our results pave the way for channel-specific analysis of ultrafast structural dynamics in chemically relevant systems, particularly for disentangling mixed reaction pathways and detecting contributions from weak channels and minority species.

Figures

Figures reproduced from arXiv: 2509.03776 by the authors.

**Figure 4.** FIG. 4. Discriminative power analysis for distinguishing [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

**Figure 5.** FIG. 5. Multidimensional analysis for structure differentiatio [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

Figures from the paper (2 more)

**Figure 7.** Figure 7: FIG. 7. (a) Photoion coincidence map corre [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

**Figure 9.** Figure 9: FIG. 9. Schematic of the double-sided velocity-map imag [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]

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Reference graph

Works this paper leans on

90 extracted references · 79 canonical work pages · cited by 1 Pith paper

[1]

A. H. Zewail, Femtochemistry: Atomic-Scale Dynamics of the Chemical Bond, The Journal of Physical Chem- istry A 104, 5660 (2000)

2000
[2]

S. P. Weathersby, G. Brown, M. Centurion, T. F. Chase, R. Coﬀee, J. Corbett, J. P. Eichner, J. C. Frisch, A. R. Fry, M. G¨ uhr, N. Hartmann, C. Hast, R. Het- tel, R. K. Jobe, E. N. Jongewaard, J. R. Lewandowski, R. K. Li, A. M. Lindenberg, I. Makasyuk, J. E. May, D. McCormick, M. N. Nguyen, A. H. Reid, X. Shen, K. Sokolowski-Tinten, T. Vecchione, S. L. Ve...

2015
[3]

M. P. Minitti, J. M. Budarz, A. Kirrander, J. S. Robin- son, D. Ratner, T. J. Lane, D. Zhu, J. M. Glownia, M. Kozina, H. T. Lemke, M. Sikorski, Y. Feng, S. Nel- son, K. Saita, B. Stankus, T. Northey, J. B. Hastings, and P. M. Weber, Imaging Molecular Motion: Femtosecond X-Ray Scattering of an Electrocyclic Chemical Reaction, Phys. Rev. Lett. 114, 255501 (2015)

2015
[4]

A. A. Ischenko, P. M. Weber, and R. J. D. Miller, Captur- ing Chemistry in Action with Electrons: Realization of Atomically Resolved Reaction Dynamics, Chemical Re- views 117, 11066 (2017)

2017
[5]

J. M. Ruddock, H. Yong, B. Stankus, W. Du, N. Goﬀ, Y. Chang, A. Odate, A. M. Carrascosa, D. Bellshaw, N. Zotev, M. Liang, S. Carbajo, J. Koglin, J. S. Robinson, S. Boutet, A. Kirrander, M. P. Minitti, and P. M. Weber, A deep UV trigger for ground-state ring-opening dynam- ics of 1,3-cyclohexadiene, Science Advances 5, eaax6625 (2019)

2019
[6]

Y. Liu, S. L. Horton, J. Yang, J. P. F. Nunes, X. Shen, T. J. A. Wolf, R. Forbes, C. Cheng, B. Moore, M. Centu- rion, K. Hegazy, R. Li, M.-F. Lin, A. Stolow, P. Hockett, T. Rozgonyi, P. Marquetand, X. Wang, and T. Weinacht, Spectroscopic and structural probing of excited-state molecular dynamics with time-resolved photoelectron spectroscopy and ultrafast ...

2020
[7]

Zhang, Z

M. Zhang, Z. Guo, X. Mi, Z. Li, and Y. Liu, Ultrafast Imaging of Molecular Dynamics Using Ultrafast Low- Frequency Lasers, X-ray Free Electron Lasers, and Elec- tron Pulses, J. Phys. Chem. Lett. 13, 1668 (2022)

2022
[8]

Filippetto, P

D. Filippetto, P. Musumeci, R. K. Li, B. J. Siwick, M. R. Otto, M. Centurion, and J. P. F. Nunes, Ultrafast elec- tron diﬀraction: Visualizing dynamic states of matter, Rev. Mod. Phys. 94, 045004 (2022)

2022

Show all 90 references

[9]

complete

The detector consisted of a set of 80 mm diameter microchannel plates (MCPs)—a funnel plate in front and a standard back plate—followed by a delay-line position-sensitive quad-anode (Roentdek DLD80). The funnel MCP signiﬁcantly enhances the de- tection eﬃciency by widening the...
[10]

Vager, R

Z. Vager, R. Naaman, and E. P. Kanter, Coulomb explo- sion imaging of small molecules, Science 244, 426 (1989)

1989
[11]

Stapelfeldt, E

H. Stapelfeldt, E. Constant, and P. B. Corkum, Wave packet structure and dynamics measured by coulomb ex- plosion, Physical Review Letters 74, 3780 (1995)

1995
[12]

Hasegawa, A

H. Hasegawa, A. Hishikawa, and K. Yamanouchi, Coin- cidence imaging of coulomb explosion of CS 2 in intense laser ﬁelds, Chemical Physics Letters 349, 57 (2001)

2001
[13]

Pitzer, M

M. Pitzer, M. Kunitski, A. S. Johnson, T. Jahnke, H. Sann, F. Sturm, L. P. H. Schmidt, H. Schmidt- B¨ ocking, R. D¨ orner, J. Stohner, J. Kiedrowski, M. Reggelin, S. Marquardt, A. Schießer, R. Berger, and M. S. Sch¨ oﬄer, Direct determination of absolute molec- ular stereochem...

2013
[14]

H. V. S. Lam, S. Yarlagadda, A. Venkatachalam, T. N. Wangjam, R. K. Kushawaha, C. Cheng, P. Svihra, A. Nomerotski, T. Weinacht, D. Rolles, and V. Kumarap- pan, Angle-dependent strong-ﬁeld ionization and frag- mentation of carbon dioxide measured using rotational wave packets, ...

2020
[15]

H. V. S. Lam, A. S. Venkatachalam, S. Bhattacharyya, K. Chen, K. Borne, E. Wang, R. Boll, T. Jahnke, V. Ku- marappan, A. Rudenko, and D. Rolles, Diﬀerentiating Three-Dimensional Molecular Structures Using Laser- Induced Coulomb Explosion Imaging, Phys. Rev. Lett. 132, 123201 (2024)

2024
[16]

H. V. S. Lam, V.-H. Hoang, A. S. Venkatachalam, S. Bhattacharyya, K. Chen, S. Jacob, S. Kudagama, T. T. Nguyen, D. Rolles, U. Thumm, A. Rudenko, and V. Kumarappan, Simultaneous imaging of vibrational, rotational, and electronic wave-packet dynamics in a tri- atomic molecule, P...

2025
[17]

E. Kukk, K. Motomura, H. Fukuzawa, K. Nagaya, and K. Ueda, Molecular dynamics of xfel-induced photo- dissociation, revealed by ion-ion coincidence measure- ments, Applied Sciences 7, 10.3390/app7050531 (2017)

2017 doi
[18]

R. Boll, J. M. Sch¨ afer, B. Richard, K. Fehre, G. Ka- stirke, Z. Jurek, M. S. Sch¨ oﬄer, M. M. Abdullah, N. An- ders, T. M. Baumann, S. Eckart, B. Erk, A. D. Fanis, R. D¨ orner, S. Grundmann, P. Grychtol, A. Hartung, M. Hofmann, M. Ilchen, L. Inhester, C. Janke, R. Jin, M. Ki...

2022
[19]

Jahnke, S

T. Jahnke, S. Mai, S. Bhattacharyya, K. Chen, R. Boll, M. E. Castellani, S. Dold, U. Fr¨ uhling, A. E. Green, M. Ilchen, R. Ingle, G. Kastirke, H. V. S. Lam, F. Lever, D. Mayer, T. Mazza, T. Mullins, Y. Ovcharenko, B. Senﬀtleben, F. Trinter, Atia-Tul-Noor, S. Usenko, A. S. Ven...
[20]

Richard, R

B. Richard, R. Boll, S. Banerjee, J. M. Sch¨ afer, Z. Ju- rek, G. Kastirke, K. Fehre, M. S. Sch¨ oﬄer, N. An- ders, T. M. Baumann, S. Eckart, B. Erk, A. De Fanis, R. D¨ orner, S. Grundmann, P. Grychtol, M. Hofmann, M. Ilchen, M. Kircher, K. Kubicek, M. Kunitski, X. Li, T. Mazz...

2025
[21]

Herwig, K

P. Herwig, K. Zawatzky, M. Grieser, O. Heber, B. Jordon-Thaden, C. Krantz, O. Novotn` y, R. Repnow, V. Schurig, D. Schwalm, and Z. Vager, Imaging the ab- solute conﬁguration of a chiral epoxide in the gas phase, Science 342, 1084 (2013)

2013
[22]

A. S. Alnaser, X. M. Tong, T. Osipov, S. Voss, C. M. Ma- harjan, P. Ranitovic, B. Ulrich, B. Shan, Z. Chang, C. D. Lin, and C. L. Cocke, Routes to control of H 2 coulomb explosion in few-cycle laser pulses, Physical Review Let- ters 93, 183202 (2004)

2004
[23]

L´ egar´ e, K

F. L´ egar´ e, K. F. Lee, I. V. Litvinyuk, P. W. Dooley, S. S. Wesolowski, P. R. Bunker, P. Dombi, F. Krausz, A. D. Bandrauk, D. M. Villeneuve, and P. B. Corkum, Laser coulomb-explosion imaging of small molecules, Physical Review A 71, 013415 (2005)

2005
[24]

Ergler, A

T. Ergler, A. Rudenko, B. Feuerstein, K. Zrost, C. D. Schr¨ oter, R. Moshammer, and J. Ullrich, Spatiotempo- ral imaging of ultrafast molecular motion: Collapse and revival of the D + 2 nuclear wave packet, Physical Review Letters 97, 193001 (2006)

2006
[25]

Cornaggia, Ultrafast coulomb explosion imaging of molecules, Laser Physics 19, 1660 (2009)

C. Cornaggia, Ultrafast coulomb explosion imaging of molecules, Laser Physics 19, 1660 (2009)

2009
[26]

L. P. H. Schmidt, T. Jahnke, A. Czasch, M. Sch¨ oﬄer, H. Schmidt-B¨ ocking, and R. D¨ orner, Spatial imaging of the H2 + vibrational wave function at the quantum limit, Phys. Rev. Lett. 108, 073202 (2012)

2012
[27]

Karimi, W.-K

R. Karimi, W.-K. Liu, and J. Sanderson, Femtosecond laser-induced coulomb explosion imaging, in Advances in Multi-Photon Processes and Spectroscopy (World Scien- tiﬁc, 2016) pp. 97–153

2016
[28]

Yatsuhashi and N

T. Yatsuhashi and N. Nakashima, Multiple ionization and coulomb explosion of molecules, molecular com- plexes, clusters and solid surfaces, Journal of Photochem- istry and Photobiology C: Photochemistry Reviews 34, 52 (2018)

2018
[29]

Hishikawa, A

A. Hishikawa, A. Matsuda, and M. Fushitani, Ultrafast reaction imaging and control by ultrashort intense laser pulses, Bulletin of the Chemical Society of Japan 93, 1293 (2020)

2020
[30]

X. Li, X. Yu, P. Ma, X. Zhao, C. Wang, S. Luo, and D. Ding, Ultrafast coulomb explosion imaging of molecules and molecular clusters, Chinese Physics B 31, 103304 (2022)

2022
[31]

Severt, Z

T. Severt, Z. L. Streeter, W. Iskandar, K. A. Larsen, A. Gatton, D. Trabert, B. Jochim, B. Griﬃn, E. G. Champenois, M. M. Brister, D. Reedy, D. Call, R. Strom, A. L. Landers, R. D¨ orner, J. B. Williams, D. S. Slaughter, R. R. Lucchese, T. Weber, C. W. McCurdy, and I. Ben- Itz...

2022
[32]

A. J. Howard, M. Britton, Z. L. Streeter, C. Cheng, R. Forbes, J. L. Reynolds, F. Allum, G. A. Mc- Cracken, I. Gabalski, R. R. Lucchese, C. W. McCurdy, T. Weinacht, and P. H. Bucksbaum, Filming enhanced ionization in an ultrafast triatomic slingshot, Communi- cations Chemistry...

2023
[33]

H. Yuan, Y. Gao, B. Yang, S. Gu, H. Lin, D. Guo, J. Liu, S. Zhang, X. Ma, and S. Xu, Coulomb Explosion Imaging of Complex Molecules Using Highly Charged Ions, Phys. Rev. Lett. 133, 193002 (2024)

2024
[34]

Dalitz, On the analysis of τ -meson data and the na- ture of the τ -meson, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 44, 1068 (1953)

R. Dalitz, On the analysis of τ -meson data and the na- ture of the τ -meson, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 44, 1068 (1953)

1953
[35]

Rajput, T

J. Rajput, T. Severt, B. Berry, B. Jochim, P. Feizol- lah, B. Kaderiya, M. Zohrabi, U. Ablikim, F. Ziaee, K. Raju P., D. Rolles, A. Rudenko, K. D. Carnes, B. D. Esry, and I. Ben-Itzhak, Native frames: Disentangling se- quential from concerted three-body fragmentation, Phys. Re...

2018
[36]

Severt, J

T. Severt, J. Rajput, B. Berry, B. Jochim, P. Feizollah, B. Kaderiya, M. Zohrabi, F. Ziaee, K. R. P., D. Rolles, A. Rudenko, K. D. Carnes, B. D. Esry, and I. Ben-Itzhak, 13 Native frames: An approach for separating sequential and concerted three-body fragmentation, Phys. Rev. ...

2024
[37]

E. Wang, S. Bhattacharyya, K. Chen, K. Borne, F. Ziaee, S. Pathak, H. V. S. Lam, A. S. Venkatachalam, X. Chen, R. Boll, T. Jahnke, A. Rudenko, and D. Rolles, Time- resolved coulomb explosion imaging unveils ultrafast ring opening of furan (2023), arXiv:2311.05099 [physics.chem- ph]

2023 arXiv
[38]

Fehre, D

K. Fehre, D. Trojanowskaja, J. Gatzke, M. Kunitski, F. Trinter, S. Zeller, L. P. H. Schmidt, J. Stohner, R. Berger, A. Czasch, O. Jagutzki, T. Jahnke, R. D¨ orner, and M. S. Sch¨ oﬄer, Absolute ion detection eﬃciencies of microchannel plates and funnel microchannel plates for ...

2018
[39]

Bhattacharyya, K

S. Bhattacharyya, K. Borne, F. Ziaee, S. Pathak, E. Wang, A. S. Venkatachalam, X. Li, N. Marshall, K. D. Carnes, C. W. Fehrenbach, T. Severt, I. Ben-Itzhak, A. Rudenko, and D. Rolles, Strong-ﬁeld-induced coulomb explosion imaging of tribromomethane, The Journal of Physical Che...

2022
[40]

X. Li, A. Rudenko, M. S. Sch¨ oﬄer, N. Anders, T. M. Baumann, S. Eckart, B. Erk, A. D. Fanis, K. Fehre, R. D¨ orner, L. Foucar, S. Grundmann, P. Grychtol, A. Hartung, M. Hofmann, M. Ilchen, C. Janke, G. Ka- stirke, M. Kircher, K. Kubicek, M. Kunitski, T. Mazza, S. Meister, N. ...

2022
[41]

X. Li, R. Boll, P. Vindel-Zandbergen, J. Gonz´ alez- V´ azquez, D. E. Rivas, S. Bhattacharyya, K. Borne, K. Chen, A. De Fanis, B. Erk, R. Forbes, A. E. Green, M. Ilchen, B. Kaderiya, E. Kukk, H. V. S. Lam, T. Mazza, T. Mullins, B. Senﬀtleben, F. Trin- ter, S. Usenko, A. S. Ven...

2025
[42]

T. Endo, S. P. Neville, V. Wanie, S. Beaulieu, C. Qu, J. Deschamps, P. Lassonde, B. E. Schmidt, H. Fujise, M. Fushitani, A. Hishikawa, P. L. Houston, J. M. Bow- man, M. S. Schuurman, F. L´ egar´ e, and H. Ibrahim, Cap- turing roaming molecular fragments in real time, Science 3...

2020
[43]

Eriksson, S

T. Eriksson, S. Bj¨ orkman, and P. H¨ oglund, Clinical phar- macology of thalidomide, European Journal of Clinical Pharmacology 57, 365 (2001)

2001
[44]

Habtemariam, Isomerism in organic compounds and drug molecules: Chemistry and signiﬁcance in biology (The Royal Society of Chemistry, 2023) pp

S. Habtemariam, Isomerism in organic compounds and drug molecules: Chemistry and signiﬁcance in biology (The Royal Society of Chemistry, 2023) pp. 149–194

2023
[45]

Patterson, M

D. Patterson, M. Schnell, and J. M. Doyle, Enantiomer- speciﬁc detection of chiral molecules via microwave spec- troscopy, Nature 497, 475 (2013)

2013
[46]

X. Zhou, Z. Wang, S. Li, X. Rong, J. Bu, Q. Liu, and Z. Ouyang, Diﬀerentiating enantiomers by directional ro- tation of ions in a mass spectrometer, Science 383, 612 (2024)

2024
[47]

J. L. Hansen, J. H. Nielsen, C. B. Madsen, A. T. Lind- hardt, M. P. Johansson, T. Skrydstrup, L. B. Mad- sen, and H. Stapelfeldt, Control and femtosecond time- resolved imaging of torsion in a chiral molecule, The Jour- nal of Chemical Physics 136, 204310 (2012)

2012
[48]

Christensen, J

L. Christensen, J. H. Nielsen, C. S. Slater, A. Lauer, M. Brouard, and H. Stapelfeldt, Using laser-induced coulomb explosion of aligned chiral molecules to de- termine their absolute conﬁguration, Phys. Rev. A 92, 033411 (2015)

2015
[49]

Fehre, S

K. Fehre, S. Eckart, M. Kunitski, M. Pitzer, S. Zeller, C. Janke, D. Trabert, J. Rist, M. Weller, A. Hartung, L. P. H. Schmidt, T. Jahnke, R. Berger, R. D¨ orner, and M. S. Sch¨ oﬄer, Enantioselective fragmentation of an achiral molecule in a strong laser ﬁeld, Science Advance...

2019
[50]

Tsitsonis, F

D. Tsitsonis, F. Trinter, J. B. Williams, K. Fehre, P. V. Demekhin, T. Jahnke, R. D¨ orner, and M. S. Sch¨ oﬄer, Enantioselective one-photon excitation of formic acid, Phys. Rev. Lett. 133, 093002 (2024)

2024
[51]

Ablikim, C

U. Ablikim, C. Bomme, H. Xiong, E. Savelyev, R. Obaid, B. Kaderiya, S. Augustin, K. Schnorr, I. Dumitriu, T. Osipov, R. Bilodeau, D. Kilcoyne, V. Kumarappan, A. Rudenko, N. Berrah, and D. Rolles, Identiﬁcation of absolute geometries of cis and trans molecular isomers by Coulom...

2016
[52]

M. Burt, K. Amini, J. W. L. Lee, L. Christiansen, R. R. Johansen, Y. Kobayashi, J. D. Pickering, C. Vallance, M. Brouard, and H. Stapelfeldt, Communication: Gas- phase structural isomer identiﬁcation by coulomb ex- plosion of aligned molecules, The Journal of Chemical Physics ...

2018
[53]

Ablikim, C

U. Ablikim, C. Bomme, T. Osipov, H. Xiong, R. Obaid, R. C. Bilodeau, N. G. Kling, I. Dumitriu, S. Augustin, S. Pathak, K. Schnorr, D. Kilcoyne, N. Berrah, and D. Rolles, A coincidence velocity map imaging spectrom- eter for ions and high-energy electrons to study inner- shell ...

2019
[54]

J. W. McManus, F. Allum, J. Featherstone, C.-S. Lam, and M. Brouard, Two-Dimensional Projected- Momentum Covariance Mapping for Coulomb Explosion Imaging, J. Phys. Chem. A 128, 3220 (2024)

2024
[55]

Pathak, R

S. Pathak, R. Obaid, S. Bhattacharyya, J. B¨ urger, X. Li, J. Tross, T. Severt, B. Davis, R. C. Bilodeau, C. A. Trallero-Herrero, A. Rudenko, N. Berrah, and D. Rolles, Diﬀerentiating and quantifying gas-phase conformational isomers using coulomb explosion imaging, The Journal ...

2020
[56]

H. V. S. Lam, A. S. Venkatachalam, S. Bhattacharyya, E. Wang, K. Borne, K. Chen, A. Rudenko, and D. Rolles, Coulomb explosion imaging: a robust method for dis- tinguishing molecular structures and tracking structural changes in photochemical reactions, in Ultrafast Non- linear...
[58]

R. J. G. B. Campello, D. Moulavi, and J. Sander, Density-Based Clustering Based on Hierarchical Density Estimates, in Advances in Knowledge Discovery and Data 14 Mining, edited by J. Pei, V. S. Tseng, L. Cao, H. Mo- toda, and G. Xu (Springer, Berlin, Heidelberg, 2013) pp. 160–172

2013
[59]

Breiman, Random forests, Machine Learning 45, 5 (2001)

L. Breiman, Random forests, Machine Learning 45, 5 (2001)

2001
[60]

Stapelfeldt, E

H. Stapelfeldt, E. Constant, H. Sakai, and P. B. Corkum, Time-resolved coulomb explosion imaging: A method to measure structure and dynamics of molecular nuclear wave packets, Physical Review A 58, 426 (1998)

1998
[61]

Rudenko, T

A. Rudenko, T. Ergler, B. Feuerstein, K. Zrost, C. D. Schr¨ oter, R. Moshammer, and J. Ullrich, Real-time ob- servation of vibrational revival in the fastest molecular system, Chemical Physics 329, 193 (2006)

2006
[62]

Schnorr, M

K. Schnorr, M. Belina, S. Augustin, H. Lindenblatt, Y. Liu, S. Meister, T. Pfeifer, G. Schmid, R. Treusch, F. Trost, P. Slav ´ ıˇ cek, and R. Moshammer, Direct track- ing of ultrafast proton transfer in water dimers, Science Advances 9, eadg7864 (2023)

2023
[63]

X. Yu, X. Zhang, X. Hu, X. Zhao, D. Ren, X. Li, P. Ma, C. Wang, Y. Wu, S. Luo, and D. Ding, Femtosecond time- resolved neighbor roles in the fragmentation dynamics of molecules in a dimer, Phys. Rev. Lett. 129, 023001 (2022)

2022
[64]

Livshits, D

E. Livshits, D. M. Bittner, F. Trost, S. Meister, H. Lin- denblatt, R. Treusch, K. Gope, T. Pfeifer, R. Baer, R. Moshammer, and D. Strasser, Symmetry-breaking dy- namics of a photoionized carbon dioxide dimer, Nature Communications 15, 6322 (2024)

2024
[65]

Walter, M

P. Walter, M. Holmes, R. Obaid, L. Amores, X. Cheng, J. P. Cryan, J. M. Glownia, X. Li, M.-F. Lin, M. L. Ng, J. Robinson, N. Shivaram, J. Yin, D. Fritz, J. James, J.- C. Castagna, and T. Osipov, The DREAM Endstation at the Linac Coherent Light Source, Applied Sciences 12, 10534 (2022)

2022
[66]

Markovic, C

B. Markovic, C. Bakalis, G. Blaj, X. Defay, D. Doering, A. Gupta, J. Hasi, C. Kenney, P. King, A. Pena-Perez, B. Reese, L. Rota, L. Ruckman, J. Segal, T. Driver, J. Cryan, C. Hansson, and A. Dragone, SparkPix- T: Spatial and Time Resolving Front-End ASIC with MHz-Rate Informat...

2023
[67]

Andr´ e, I

T. Andr´ e, I. Dawod, S. Cardoch, E. De Santis, N. m. c. T ˆ ımneanu, and C. Caleman, Protein structure classiﬁ- cation based on x-ray-laser-induced coulomb explosion, Phys. Rev. Lett. 134, 128403 (2025)

2025
[68]

O. V. Prezhdo, Advancing Physical Chemistry with Ma- chine Learning, J. Phys. Chem. Lett. 11, 9656 (2020)

2020
[69]

M. W. Dorrity, L. M. Saunders, C. Queitsch, S. Fields, and C. Trapnell, Dimensionality reduction by UMAP to visualize physical and genetic interactions, Nat Commun 11, 1537 (2020)

2020
[70]

Sheng Ye and Lvshuai Zhu and Zhicheng Zhao and Fan Wu and Zhipeng Li and BinBin Wang and Kai Zhong and Changyin Sun and Shaul Mukamel and Jun Jiang , Ai protocol for retrieving protein dynamic structures from two-dimensional infrared spectra, Proceedings of the Na- tional Acad...

2025
[71]

van der Maaten and G

L. van der Maaten and G. Hinton, Visualizing high- dimensional data using t-SNE, Journal of Machine Learn- ing Research 9, 2579 (2008)

2008
[72]

R. A. FISHER, The use of multiple measurements in tax- onomic problems, Annals of Eugenics 7, 179 (1936)

1936
[73]

P. J. Rousseeuw, Silhouettes: A graphical aid to the in- terpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics 20, 53 (1987)

1987
[75]

McInnes, J

L. McInnes, J. Healy, and S. Astels, hdbscan: Hierar- chical density based clustering, Journal of Open Source Software 2, 205 (2017)

2017
[76]

Pedregosa, G

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cour- napeau, M. Brucher, M. Perrot, and E. Duchesnay, Scikit-learn: Machine learning in python, Journal of Ma- chine Lear...

2011
[77]

Experimental data 12
[78]

Complete

Simulated data 12 E. Supporting ﬁgures for Random Forest Classiﬁcation analysis 14 References 16 ∗ huynhlam@ksu.edu 2 I. ON THE POSSIBILITY OF EXTENDING THE CURRENT FRAMEWORK TO LA RGER MOLECULAR SYSTEMS “Complete” CEI is particularly powerful because it delivers, in e very si...
[79]

complete

and next-generation detectors [ 2] optimized for multiparticle coincidence experiments at MHz repe tition rates. B. Data analysis pipelines Other “complete” CEI channels : • Exploring options in data analysis, such as analyzing channels where fr agments with the same m/q have ...
[80]

random state

and Davies–Bouldin Index [ 8]. The resulting metrics (see table below) indicate that our UMAP anal ysis is highly stable and robust. UMAP Silhouette Score 0.84 ± 0.04 Davies-Bouldin Index 0.22 ± 0.05 Similarly, due to the stochastic nature of the Random Forest Classiﬁe r—origi...
[81]

S13, we evaluate the data reduction across three representative fragment ation channels

Experimental data In Fig. S13, we evaluate the data reduction across three representative fragment ation channels. The Newton plots (a–c), UMAP embeddings (d–f), and distributions of angle between the two chlorine fragments (g–i) all show that fragmentation channels with highe...
[82]

Simulated data In this section, we systematically broadened the initial real-space distributions and increased the kinetic energy for twisted-1,2-DCE and 1,1-DCE in the simulation. Across conditions, higher total ﬁnal -state charge yields more robust 13 separations of geometri...
[83]

Walter, M

P. Walter, M. Holmes, R. Obaid, L. Amores, X. Cheng, J. P. Crya n, J. M. Glownia, X. Li, M.-F. Lin, M. L. Ng, J. Robinson, N. Shivaram, J. Yin, D. Fritz, J. James, J.-C. Castagna, and T. Os ipov, The DREAM Endstation at the Linac Coherent Light Source, Applied Sciences 12, 105...

2022
[84]

Markovic, C

B. Markovic, C. Bakalis, G. Blaj, X. Defay, D. Doering, A. Gupta, J . Hasi, C. Kenney, P. King, A. Pena-Perez, B. Reese, L. Rota, L. Ruckman, J. Segal, T. Driver, J. Cryan, C. Hansson, and A. Dragone, SparkPix-T: Spatial and Time Resolving Front-End ASIC with MHz-Rate Informat...

2023
[85]

X. Li, R. Boll, P. Vindel-Zandbergen, J. Gonz´ alez-V´ azquez, D. E. Rivas, S. Bhattacharyya, K. Borne, K. Chen, A. De Fanis, B. Erk, R. Forbes, A. E. Green, M. Ilchen, B. Kaderiya, E. Kukk, H. V . S. Lam, T. Mazza, T. Mullins, B. Senﬀtleben, F. Trinter, S. Usenko, A. S. Venka...

2025
[86]

Richard, R

B. Richard, R. Boll, S. Banerjee, J. M. Sch¨ afer, Z. Jurek, G. Kas tirke, K. Fehre, M. S. Sch¨ oﬄer, N. Anders, T. M. Baumann, S. Eckart, B. Erk, A. De Fanis, R. D¨ orner, S. Grundmann, P. G rychtol, M. Hofmann, M. Ilchen, M. Kircher, K. Kubicek, M. Kunitski, X. Li, T. Mazza,...

2025
[87]

van der Maaten and G

L. van der Maaten and G. Hinton, Visualizing high-dimens ional data using t-SNE, Journal of Machine Learning Research 9, 2579 (2008)

2008
[88]

McInnes, J

L. McInnes, J. Healy, and J. Melville, UMAP: Uniform Manifol d Approximation and Projection for Dimension Reduction (2018), arXiv:1802.03426 [stat.ML]

2018 arXiv
[89]

P. J. Rousseeuw, Silhouettes: A graphical aid to the interp retation and validation of cluster analysis, Journal of Compu- tational and Applied Mathematics 20, 53 (1987)

1987
[90]

D. L. Davies and D. W. Bouldin, A Cluster Separation Measure, IEEE Transactions on Pattern Analysis and Machine Intelligence P AMI-1, 224 (1979)

1979
[91]

Andr´ e, I

T. Andr´ e, I. Dawod, S. Cardoch, E. De Santis, N. T ˆ ımneanu, a nd C. Caleman, Protein structure classiﬁcation based on x-ray-laser-induced coulomb explosion, Phys. Rev. Lett. 134, 128403 (2025)

2025
[92]

M. W. Dorrity, L. M. Saunders, C. Queitsch, S. Fields, and C. T rapnell, Dimensionality reduction by UMAP to visualize physical and genetic interactions, Nat Commun 11, 1537 (2020)

2020

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

A. H. Zewail, Femtochemistry: Atomic-Scale Dynamics of the Chemical Bond, The Journal of Physical Chem- istry A 104, 5660 (2000)

2000

[2] [2]

S. P. Weathersby, G. Brown, M. Centurion, T. F. Chase, R. Coﬀee, J. Corbett, J. P. Eichner, J. C. Frisch, A. R. Fry, M. G¨ uhr, N. Hartmann, C. Hast, R. Het- tel, R. K. Jobe, E. N. Jongewaard, J. R. Lewandowski, R. K. Li, A. M. Lindenberg, I. Makasyuk, J. E. May, D. McCormick, M. N. Nguyen, A. H. Reid, X. Shen, K. Sokolowski-Tinten, T. Vecchione, S. L. Ve...

2015

[3] [3]

M. P. Minitti, J. M. Budarz, A. Kirrander, J. S. Robin- son, D. Ratner, T. J. Lane, D. Zhu, J. M. Glownia, M. Kozina, H. T. Lemke, M. Sikorski, Y. Feng, S. Nel- son, K. Saita, B. Stankus, T. Northey, J. B. Hastings, and P. M. Weber, Imaging Molecular Motion: Femtosecond X-Ray Scattering of an Electrocyclic Chemical Reaction, Phys. Rev. Lett. 114, 255501 (2015)

2015

[4] [4]

A. A. Ischenko, P. M. Weber, and R. J. D. Miller, Captur- ing Chemistry in Action with Electrons: Realization of Atomically Resolved Reaction Dynamics, Chemical Re- views 117, 11066 (2017)

2017

[5] [5]

J. M. Ruddock, H. Yong, B. Stankus, W. Du, N. Goﬀ, Y. Chang, A. Odate, A. M. Carrascosa, D. Bellshaw, N. Zotev, M. Liang, S. Carbajo, J. Koglin, J. S. Robinson, S. Boutet, A. Kirrander, M. P. Minitti, and P. M. Weber, A deep UV trigger for ground-state ring-opening dynam- ics of 1,3-cyclohexadiene, Science Advances 5, eaax6625 (2019)

2019

[6] [6]

Y. Liu, S. L. Horton, J. Yang, J. P. F. Nunes, X. Shen, T. J. A. Wolf, R. Forbes, C. Cheng, B. Moore, M. Centu- rion, K. Hegazy, R. Li, M.-F. Lin, A. Stolow, P. Hockett, T. Rozgonyi, P. Marquetand, X. Wang, and T. Weinacht, Spectroscopic and structural probing of excited-state molecular dynamics with time-resolved photoelectron spectroscopy and ultrafast ...

2020

[7] [7]

Zhang, Z

M. Zhang, Z. Guo, X. Mi, Z. Li, and Y. Liu, Ultrafast Imaging of Molecular Dynamics Using Ultrafast Low- Frequency Lasers, X-ray Free Electron Lasers, and Elec- tron Pulses, J. Phys. Chem. Lett. 13, 1668 (2022)

2022

[8] [8]

Filippetto, P

D. Filippetto, P. Musumeci, R. K. Li, B. J. Siwick, M. R. Otto, M. Centurion, and J. P. F. Nunes, Ultrafast elec- tron diﬀraction: Visualizing dynamic states of matter, Rev. Mod. Phys. 94, 045004 (2022)

2022