Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test | Paper Citation Record

Observation 448b558f-c354-4d25-9070-241b24bb3b72 · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 1

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Observation c165e576-596e-4ba9-8d45-f997070f2bac · outbound

This paper cites A.; Cederbaum, L.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test A.; Cederbaum, L

Reference 2

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Observation d740e388-64ab-4d82-9054-f46d2e79c20a · outbound

This paper cites R.; White, A.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test R.; White, A

Reference 3

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Observation da0b8153-bb8e-4895-837a-30082390e615 · outbound

This paper cites J.; Plasser, F.; Gonz´ alez, L.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test J.; Plasser, F.; Gonz´ alez, L

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:52.587795Z

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Observation d1e4188d-7383-419a-a369-3b5e58bdb5e3 · outbound

This paper cites What Controls the Quality of Photodynamical Simulations? Electronic Structure Ver- sus Nonadiabatic Algorithm.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test What Controls the Quality of Photodynamical Simulations? Electronic Structure Ver- sus Nonadiabatic Algorithm

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:52.739007Z

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Observation 9bb1bcb0-12b3-4c49-87e2-fb985faae694 · outbound

This paper cites V.; Jacquemin, D.; Vacher, M.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test V.; Jacquemin, D.; Vacher, M

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:52.909466Z

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Observation 8d242fde-f381-46fb-9efd-3e6795edc6e8 · outbound

This paper cites Perspective on a challenge: predicting the photochemistry of cyclobutanone.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Perspective on a challenge: predicting the photochemistry of cyclobutanone

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:53.013983Z

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Observation ca0349f2-f40b-4623-bd31-843eeb5a1cd8 · outbound

This paper cites O.; Taylor, P.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test O.; Taylor, P

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:53.092385Z

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Observation a363c058-d063-4ff1-ae8e-eb351d5b1516 · outbound

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Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:53.166139Z

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Observation 0ae09a18-7d18-4605-87b4-8d51600c131b · outbound

This paper cites A.; Roos, B.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test A.; Roos, B

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:53.226847Z

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Observation 315ee525-33f3-4aee-befc-7c5c79ffd989 · outbound

This paper cites O.; Serrano- Andr´ es, L.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test O.; Serrano- Andr´ es, L

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:53.338248Z

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Observation 43a1f980-ba7b-4476-b756-5388e89afe03 · outbound

This paper cites Com- munication: Extended multi-state complete active space second-order perturbation theory: Energy and nuclear gradients.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Com- munication: Extended multi-state complete active space second-order perturbation theory: Energy and nuclear gradients

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:53.431129Z

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Observation 5e804ead-5dff-4009-b63e-786ef40fe9af · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:53.635240Z

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Observation c90cf09c-10db-4f51-82b8-55a30790dd6e · outbound

This paper cites Surface Hopping Dynamics with Correlated Single-Reference Methods: 9H-Adenine as a Case Study.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Surface Hopping Dynamics with Correlated Single-Reference Methods: 9H-Adenine as a Case Study

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:53.753744Z

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Observation 38ca0410-5ee7-4920-846f-2356c9fc4bb8 · outbound

This paper cites L.; Zhu, C.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test L.; Zhu, C

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:53.868088Z

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Observation ba1f806a-df96-4d1d-8a6c-62594895306b · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:53.981936Z

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Observation fbec3880-5808-4f63-8dfa-b19d1bdb41ff · outbound

This paper cites A note on the convergence of mul- ticonfigurational many-body perturbation theory.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test A note on the convergence of mul- ticonfigurational many-body perturbation theory

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:54.093598Z

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Observation 3bc8735e-13f5-4d9b-a0ba-6a702c47dfbe · outbound

This paper cites P.; Rancurel, P.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test P.; Rancurel, P

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:54.252629Z

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Observation c458d18e-b1a6-438a-80c7-833fb5cb3d3e · outbound

This paper cites Excited States with Selected Configuration Interaction- Quantum Monte Carlo: Chemically Accurate Excita- tion Energies and Geometries.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Excited States with Selected Configuration Interaction- Quantum Monte Carlo: Chemically Accurate Excita- tion Energies and Geometries

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:54.393386Z

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Observation 4bd8606e-3bc1-4103-867c-a8d4c403d723 · outbound

This paper cites D.; Neuscamman, E.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test D.; Neuscamman, E

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:54.504529Z

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Observation 6f3cf140-3a43-4264-be7d-436bf4b77833 · outbound

This paper cites Tailoring CIPSI Expansions for QMC Calculations of Electronic Excitations: The Case Study of Thiophene.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Tailoring CIPSI Expansions for QMC Calculations of Electronic Excitations: The Case Study of Thiophene

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:54.653214Z

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Observation 1270089b-fdd3-436c-bf8e-c50d671ca8a9 · outbound

This paper cites Reference Excitation Energies of Increasingly Large Molecules: A QMC Study of Cyanine Dyes.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Reference Excitation Energies of Increasingly Large Molecules: A QMC Study of Cyanine Dyes

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:54.843399Z

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Observation ac2709fa-7f01-4823-9cce-0721bf6d3dd9 · outbound

This paper cites L.; Moroni, S.; Sce- mama, A.; Filippi, C.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test L.; Moroni, S.; Sce- mama, A.; Filippi, C

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:54.935506Z

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Observation 837f320a-0de4-4063-a7c8-ad387326f2fa · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:55.053623Z

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Observation daf45c24-6b66-4da6-b160-7c416908f925 · outbound

This paper cites T.; Chmiela, S.; Sauceda, H.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test T.; Chmiela, S.; Sauceda, H

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:55.175683Z

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Observation 6e5f136a-2855-4d09-a0c4-842f4e193bee · outbound

This paper cites High- Pressure Hydrogen by Machine Learning and Quantum Monte Carlo.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test High- Pressure Hydrogen by Machine Learning and Quantum Monte Carlo

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:55.258274Z

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Observation 41111d32-75db-4d9b-8f70-c04a12022950 · outbound

This paper cites Stable Solid Molecular Hydrogen above 900 K from a Machine-Learned Potential Trained with Diffu- sion Quantum Monte Carlo.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Stable Solid Molecular Hydrogen above 900 K from a Machine-Learned Potential Trained with Diffu- sion Quantum Monte Carlo

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:55.362309Z

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Observation 157ba5f0-2321-4ee4-ba07-224b8d9175d5 · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:55.461777Z

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Observation fa498176-7697-4883-8d72-26a364aa0bcf · outbound

This paper cites A.; Krogel, J.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test A.; Krogel, J

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:55.616148Z

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Observation fccdfa56-fcd3-4e83-ade6-d2d3fea9ac5c · outbound

This paper cites Principal Deuterium Hugoniot via Quantum Monte Carlo and ∆ -Learning.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Principal Deuterium Hugoniot via Quantum Monte Carlo and ∆ -Learning

Reference 30

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Observation 7186f524-0e17-4782-af2b-0cbb68e66bb2 · outbound

This paper cites Accurate Quan- tum Monte Carlo Forces for Machine-Learned Force Fields: Ethanol as a Benchmark.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Accurate Quan- tum Monte Carlo Forces for Machine-Learned Force Fields: Ethanol as a Benchmark

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:55.887378Z

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Observation 0602b932-d264-4f23-b05f-ee14013ca608 · outbound

This paper cites Semiclassical Simulations of Azomethane Photochemistry in the Gas Phase and in Solution.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Semiclassical Simulations of Azomethane Photochemistry in the Gas Phase and in Solution

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:56.002399Z

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Observation c14bc4a9-0ef2-4ee1-ab35-a180c45af535 · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 33

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Observation 1bf34715-fa0a-4cc7-85d6-bb72611483f3 · outbound

This paper cites Azomethane: Nonadiabatic Photodynamical Simulations in Solution.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Azomethane: Nonadiabatic Photodynamical Simulations in Solution

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:56.293532Z

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Observation cca94a5b-320b-48cd-8005-ca43002b1e86 · outbound

This paper cites W.-G.; Abou-Zied, O.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test W.-G.; Abou-Zied, O

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:56.450498Z

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Observation faf8a144-07ac-43f0-a0dc-9972b23509a1 · outbound

This paper cites W.-G.; Zewail, A.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test W.-G.; Zewail, A

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:56.616139Z

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Observation 21ae091f-778e-42b4-8cb7-c9a02d24ad45 · outbound

This paper cites W.; Longfellow, C.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test W.; Longfellow, C

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Observation 541b8610-d4dd-4063-85c5-eee76e62d9c6 · outbound

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Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 38

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Observation e262d3be-49e1-4fa2-9b69-7d28b7f18c10 · outbound

This paper cites Quantum Monte Carlo methods.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Quantum Monte Carlo methods

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:57.057848Z

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Observation 1bac28cc-eb67-40ae-88d6-c86a2b1287ef · outbound

This paper cites M.; Zubarev, D.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test M.; Zubarev, D

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:57.225471Z

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Observation cc9b4e8c-6df2-4ffe-a503-6355a10b8fad · outbound

This paper cites J.; Barnett, R.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test J.; Barnett, R

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:57.348110Z

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Observation 4fcb1e7d-2589-45fd-bbe6-37ed5d74f345 · outbound

This paper cites Zero-Variance Zero-Bias Princi- ple for Observables in Quantum Monte Carlo: Applica- tion to Forces.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Zero-Variance Zero-Bias Princi- ple for Observables in Quantum Monte Carlo: Applica- tion to Forces

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:57.466093Z

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Observation 03f9daa4-c8b9-467e-9419-975fd582d470 · outbound

This paper cites Simple formalism for efficient derivatives and multi-determinant expansions in quantum Monte Carlo.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Simple formalism for efficient derivatives and multi-determinant expansions in quantum Monte Carlo

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:57.635012Z

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Observation 03a42ea0-5742-4164-9d33-3bf839c6afa0 · outbound

This paper cites D.; Trail, J.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test D.; Trail, J

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:57.809130Z

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Observation 297b38e3-c796-4df1-ac73-1f0bd74a1e0c · outbound

This paper cites Stable Liquid Hydrogen at High Pressure by a Novel Ab Initio Molecular-Dynamics Calculation.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Stable Liquid Hydrogen at High Pressure by a Novel Ab Initio Molecular-Dynamics Calculation

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:57.923795Z

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Observation 5306ac90-d2ec-469d-b264-8206994effc6 · outbound

This paper cites A.; Kleiner, K.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test A.; Kleiner, K

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:58.032982Z

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Observation 40486649-f611-4b92-89df-378693136989 · outbound

This paper cites Op- timizing excited states in quantum Monte Carlo: A re- assessment of double excitations.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Op- timizing excited states in quantum Monte Carlo: A re- assessment of double excitations

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:58.142605Z

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Observation db04d236-d3fb-478a-a05e-c638ae77ff79 · outbound

This paper cites J.; Shepard, S.; Sloot- man, E.; Cuzzocrea, A.; Azizi, V.; Lopez-Tarifa, P.; Re- naud, N.; Umrigar, C.; Moroni, S.; Filippi, C.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test J.; Shepard, S.; Sloot- man, E.; Cuzzocrea, A.; Azizi, V.; Lopez-Tarifa, P.; Re- naud, N.; Umrigar, C.; Moroni, S.; Filippi, C

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:58.278618Z

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Observation b4320919-b83c-4904-b272-bde923f5892e · outbound

This paper cites Energy-consistent pseudopotentials for quantum Monte Carlo calculations.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Energy-consistent pseudopotentials for quantum Monte Carlo calculations

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:58.442924Z

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Observation 5d9aa03e-6c3b-4da5-a318-1830b73257b6 · outbound

This paper cites For the hydrogen atom, we use a more accurate BFD pseudopotential and basis set, which is included in the CHAMP repository.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test For the hydrogen atom, we use a more accurate BFD pseudopotential and basis set, which is included in the CHAMP repository

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:58.580796Z

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Observation b745aeeb-d134-4ca0-8152-047dbc85be37 · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:58.696698Z

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Observation 95088f83-465d-40a0-bdbe-34db942d82af · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:58.833765Z

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Observation 35ee3fb2-ee1f-455e-95ad-9c1059aa3851 · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:58.962185Z

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Observation 0357700d-a3e5-45a1-bbed-aaa0bd1ad1bf · outbound

This paper cites We employ different electron-nucleus Jas- trow factors to describe the correlation of an electron with C, N, and H.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test We employ different electron-nucleus Jas- trow factors to describe the correlation of an electron with C, N, and H

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:59.134698Z

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Observation bca8ff79-4278-45af-a8e7-8ef94f1673af · outbound

This paper cites Weak binding between two aromatic rings: Feeling the van der Waals attraction by quantum Monte Carlo methods.J.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Weak binding between two aromatic rings: Feeling the van der Waals attraction by quantum Monte Carlo methods.J

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:59.308469Z

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Observation 2ebc7c6d-f79e-4e68-95d4-9f9a1566c872 · outbound

This paper cites J.; Chan, G.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test J.; Chan, G

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Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:59.518289Z

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Observation d59502cc-f44e-4a7b-8bcb-9f31e7cd006c · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

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Observation a5fbfe91-ad7b-4e0f-87f6-e5ff8f5dadee · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:59.690690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c2392b6-c7cc-408b-ae02-919406e2fcab · outbound

This paper cites O.; ˚Ake Malmqvist, P.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test O.; ˚Ake Malmqvist, P

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:59.776412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abbc3c88-e7a3-4120-a538-72b7e3c85d35 · outbound

This paper cites Multiconfiguration per- turbation theory with imaginary level shift.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Multiconfiguration per- turbation theory with imaginary level shift

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:59.867105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ccc7d70-ce42-48c8-a760-96c313f8ead8 · outbound

This paper cites K.; An, H.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test K.; An, H

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:59.922558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8234d6b1-eb62-4ccc-b760-cfbe30a4c9f3 · outbound

This paper cites do Casal, M.; Toldo, J.; Pinheiro Jr, M.; Barbatti, M.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test do Casal, M.; Toldo, J.; Pinheiro Jr, M.; Barbatti, M

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-01T21:19:59.975859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55ab15b5-6395-4273-b6d7-00a75413963d · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:00.068159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7de179d-d0dc-45b7-bec1-4e28aefb14aa · outbound

This paper cites Nonadiabatic dynamics with trajectory sur- face hopping method.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Nonadiabatic dynamics with trajectory sur- face hopping method

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:00.161238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcd6000a-b09c-4477-b58e-d0adc2880be2 · outbound

This paper cites Nonadiabatic Dy- namics: The SHARC Approach.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Nonadiabatic Dy- namics: The SHARC Approach

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:00.272004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df5e9a93-8547-4890-97bc-64a2348921f0 · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:00.358759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b56b2d7-0500-427e-a54f-b0414a6578ac · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:00.476878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 960bc5a7-d31c-4020-b28b-6e506297e9a8 · outbound

This paper cites Including quan- tum decoherence in surface hopping.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Including quan- tum decoherence in surface hopping

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:00.537616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c93ebd3-4ce6-4ee5-8b5d-d072db2201a2 · outbound

This paper cites Velocity Adjustment in Surface Hopping: Ethylene as a Case Study of the Maximum Error Caused by Direction Choice.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Velocity Adjustment in Surface Hopping: Ethylene as a Case Study of the Maximum Error Caused by Direction Choice

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:00.604353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f456dee-a2fc-44fb-8e14-278e085a0a5a · outbound

This paper cites P.; Springborg, M.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test P.; Springborg, M

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:00.667292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e7c928f-cf76-479c-ab76-4325f40d9c6c · outbound

This paper cites Constructing high-dimensional neural network potentials: A tutorial review.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Constructing high-dimensional neural network potentials: A tutorial review

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:00.740421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13fd947b-c1c4-4237-9a82-74afd183fe72 · outbound

This paper cites Machine learn- ing molecular dynamics for the simulation of infrared spectra.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Machine learn- ing molecular dynamics for the simulation of infrared spectra

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:00.777474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6f0fa04-aed8-4e68-b384-865e16e86946 · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:00.843311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b9876ca-d52c-40ac-8a71-43e9a0d40a19 · outbound

This paper cites SpaiNN: equiv- ariant message passing for excited-state nonadiabatic 13 molecular dynamics.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test SpaiNN: equiv- ariant message passing for excited-state nonadiabatic 13 molecular dynamics

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:00.888863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64b18f87-d2a9-4f71-837a-f8764d56d569 · outbound

This paper cites Equivariant mes- sage passing for the prediction of tensorial properties and molecular spectra.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Equivariant mes- sage passing for the prediction of tensorial properties and molecular spectra

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:00.967091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54371e44-1cb1-447f-84a4-e5602601a0de · outbound

This paper cites A.; Weisman, R.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test A.; Weisman, R

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:01.004852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7386ca61-702f-4a75-baa3-6f4aa6de9be9 · outbound

This paper cites Energy content of methyl radicals produced in the UV photodissociation of azomethane.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Energy content of methyl radicals produced in the UV photodissociation of azomethane

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:01.085845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f843654-e31f-4364-bfb2-3c754622bea4 · outbound

This paper cites S.; North, S.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test S.; North, S

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:01.148819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf381677-fe3d-4499-9a36-da12d004abba · outbound

This paper cites The concerted photodissociation of azomethane at 193 nm.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test The concerted photodissociation of azomethane at 193 nm

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:01.257959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e34aa1d1-be4f-438a-8fee-59a0f15c6ace · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:01.354653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0104034b-ad97-497c-8806-2e08c5accee7 · outbound

This paper cites F.; Steel, C.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test F.; Steel, C

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:01.446069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9330a7c7-1112-41fc-9a00-21a46c6aeab9 · outbound

This paper cites B.; Hart, R.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test B.; Hart, R

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:01.566848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7ab8541-c68a-4130-a372-b8fa959fe505 · outbound

This paper cites G.; Aquino, A.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test G.; Aquino, A

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:01.671851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 670e5d8c-c97d-4d7a-ab5a-f640cc04deb4 · outbound

This paper cites J.; Musia l, M.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test J.; Musia l, M

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:01.776444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c559317-d23e-460d-8daa-bab0bbcc1cb9 · outbound

This paper cites J.; Taylor, P.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test J.; Taylor, P

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:01.832390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2169a1de-7483-4226-8c99-41c17729e6fc · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:01.929866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1afba365-e618-4cb8-93a9-846e550c366a · outbound

This paper cites an unresolved cited work.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Unresolved cited work

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:02.002793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd605c45-8c50-4961-bb54-0f56f9b5f5d2 · outbound

This paper cites R.; Truhlar, D.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test R.; Truhlar, D

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:02.087212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36ee2d2a-cd98-471a-8114-9f2fc6f36bb1 · outbound

This paper cites L.; Gelin, M.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test L.; Gelin, M

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:02.152328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc4831c3-f351-48dc-b114-491af69681d5 · outbound

This paper cites Effect of Initial Conditions Sampling on Surface Hopping Simulations in the Ultrashort and Picosecond Time Range.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Effect of Initial Conditions Sampling on Surface Hopping Simulations in the Ultrashort and Picosecond Time Range

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:02.220631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f74db4e2-b911-4a94-83c4-be4381ad97d8 · outbound

This paper cites Macdonald, B.; Guan, Y.; Thompson, D.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Macdonald, B.; Guan, Y.; Thompson, D

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:02.265621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bcf0ad3-4342-4329-a77d-d06c7473a1c1 · outbound

This paper cites Simulations of molecular photodynamics in long timescales.

Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test Simulations of molecular photodynamics in long timescales

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-01T21:20:02.316105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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