Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning | Paper Citation Record

Observation 2d13866a-383f-4d81-b6d2-642c0ee8c139 · outbound

This paper cites Generalized neural-network representation of high-dimensional potential-energy surfaces.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Generalized neural-network representation of high-dimensional potential-energy surfaces

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.957514Z

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Observation d33d3d99-0ef4-4b14-9fad-cf6d4c017e1f · outbound

This paper cites Schnet: A continuous-filter convolutional neural network for modeling quantum interactions.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Schnet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.954704Z

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Observation 9aef8c61-94ee-44db-99e5-89530dbe11f6 · outbound

This paper cites Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.951328Z

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This paper cites Scaling deep learning for materials discovery.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Scaling deep learning for materials discovery

Reference 4

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Observation e3c97da1-1d30-433a-96b9-03db14fa6491 · outbound

This paper cites Uma: A family of universal models for atoms.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Uma: A family of universal models for atoms

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-20T16:28:38.099233Z

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Observation 812755db-34bd-4c72-b5a2-1ab65b9d28e4 · outbound

This paper cites EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-20T16:28:38.094972Z

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Observation 107510e5-f9ac-4de7-a112-912e3aacf1e7 · outbound

This paper cites E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.944560Z

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Observation 00e37da8-3b28-4a28-8e76-4eb10a18cc35 · outbound

This paper cites Learning local equivariant representations for large-scale atomistic dynamics.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Learning local equivariant representations for large-scale atomistic dynamics

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.941260Z

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Observation 0d09a1d1-434f-4e2f-b7a5-95779a2e80ea · outbound

This paper cites A generative model for inorganic materials design.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning A generative model for inorganic materials design

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.937143Z

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Observation 58083c3e-6568-4c1e-a6c5-1b73c12c4935 · outbound

This paper cites Crystal Diffusion Variational Autoencoder for Periodic Material Generation.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Crystal Diffusion Variational Autoencoder for Periodic Material Generation

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-20T16:28:38.116440Z

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Observation bef1c50d-1b26-4a01-8f52-8720aeec24e3 · outbound

This paper cites MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-20T16:28:38.109685Z

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Observation 96deb405-fa7f-49bf-9653-206896f463aa · outbound

This paper cites CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.134106Z

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Observation 86c10922-2af3-4719-9960-995dfce5be9f · outbound

This paper cites MACE: Higher order equivariant message passing neural networks for fast and accurate force fields.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning MACE: Higher order equivariant message passing neural networks for fast and accurate force fields

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.136229Z

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Observation e240f85a-85ec-43b2-b07a-08007937c492 · outbound

This paper cites Atomistic line graph neural network for improved materials property predictions.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Atomistic line graph neural network for improved materials property predictions

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.138193Z

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Observation 2f08087f-385f-427c-abcd-e0e87c52d70a · outbound

This paper cites Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.123273Z

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Observation a0af7764-4f1a-4115-ab0c-d443d04dc355 · outbound

This paper cites Computational methods for long-timescale atomistic simula- tions.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Computational methods for long-timescale atomistic simula- tions

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.125831Z

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Observation 2277c931-192e-4bde-aa27-ebede62b8f4a · outbound

This paper cites Materials: Engineering, Science, Processing and Design.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Materials: Engineering, Science, Processing and Design

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.127706Z

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Observation e96d0290-8cec-4a1a-a6d7-f93ffb66795b · outbound

This paper cites Masset, R.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Masset, R

Reference 18

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Observation 2335e8dd-6584-48e8-8f63-f290febec398 · outbound

This paper cites Frenkel and B.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Frenkel and B

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.110514Z

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Observation 54e35958-569a-4ea5-95b2-1fa59ba70699 · outbound

This paper cites Temperature-accelerated dynamics for simulation of infrequent events.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Temperature-accelerated dynamics for simulation of infrequent events

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.112802Z

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Observation cfb14574-a9bf-4214-9da0-7f6deb44d76a · outbound

This paper cites A climbing image nudged elastic band method for finding saddle points and minimum energy paths.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning A climbing image nudged elastic band method for finding saddle points and minimum energy paths

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.114845Z

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Observation b7b120da-d73a-4a90-8956-c29977979d54 · outbound

This paper cites Introduction to the kinetic Monte Carlo method.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Introduction to the kinetic Monte Carlo method

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.118914Z

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Observation 96fb7676-1d26-42d2-890c-c5c30eb6058e · outbound

This paper cites Escaping free-energy minima.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Escaping free-energy minima

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.139993Z

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This paper cites Collective variable discovery in the age of machine learning: reality, hype and everything in between.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Collective variable discovery in the age of machine learning: reality, hype and everything in between

Reference 24

Resolution: verified exact; doi, observed 2026-05-20T16:28:37.570028Z

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Observation a0ed7308-c55d-4d57-b6be-7f7b25f9b130 · outbound

This paper cites Event-based relaxation of continuous disordered systems.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Event-based relaxation of continuous disordered systems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.103971Z

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Observation d9e7d258-f29c-4f91-94e5-a3c77d14e6bd · outbound

This paper cites Traveling through potential energy landscapes of disordered materials: The activation-relaxation technique.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Traveling through potential energy landscapes of disordered materials: The activation-relaxation technique

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.095302Z

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Observation 6d01a245-4438-46b8-b606-af3f0355a6c9 · outbound

This paper cites A dimer method for finding saddle points on high dimensional potential surfaces using only first derivatives.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning A dimer method for finding saddle points on high dimensional potential surfaces using only first derivatives

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.090334Z

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This paper cites Denoising diffusion probabilistic models.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Denoising diffusion probabilistic models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.092657Z

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Observation 6dad3895-6f6c-4afb-a457-d901b3f32dff · outbound

This paper cites Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.097692Z

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Observation 653fcb46-d9f2-4c42-9298-15fb82d2df0e · outbound

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Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-05-20T16:28:39.101804Z

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Observation 7d67b0d0-3e0e-4946-b170-e166e5394e60 · outbound

This paper cites High- κ gate dielectrics: Current status and materials properties considerations.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning High- κ gate dielectrics: Current status and materials properties considerations

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.087338Z

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This paper cites High-K materials and metal gates for CMOS applications.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning High-K materials and metal gates for CMOS applications

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.106613Z

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Observation f653ba7a-c77d-4534-8a17-422e205eda1b · outbound

This paper cites Ab initio investigation of charge trapping across the crystalline-Si–amorphous-Si O 2 interface.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Ab initio investigation of charge trapping across the crystalline-Si–amorphous-Si O 2 interface

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.129750Z

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Observation 0fed4d4f-6406-41f1-9483-6fef1aa233b3 · outbound

This paper cites Ultrathin (¡ 4 nm) SiO2 and Si–O–N gate dielectric layers for silicon microelectronics: Understanding the processing, structure, and physical and electrical limits.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Ultrathin (¡ 4 nm) SiO2 and Si–O–N gate dielectric layers for silicon microelectronics: Understanding the processing, structure, and physical and electrical limits

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.064922Z

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Observation d1239c15-dfcd-4587-8b5f-f6c6efb16c87 · outbound

This paper cites Limiting Si/SiO2 interface roughness resulting from thermal oxidation.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Limiting Si/SiO2 interface roughness resulting from thermal oxidation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.058365Z

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Observation 1d828849-d354-4365-ab78-c7adbc34900f · outbound

This paper cites Dynamic observations of interface propagation during silicon oxidation.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Dynamic observations of interface propagation during silicon oxidation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.052362Z

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Observation 03b4c914-d0bf-48f0-998e-816658bee6b3 · outbound

This paper cites What can electron paramagnetic resonance tell us about the Si/SiO 2 system?.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning What can electron paramagnetic resonance tell us about the Si/SiO 2 system?

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.054341Z

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Observation 6d151a6c-0471-40ca-b3e6-0127c5e8f6b5 · outbound

This paper cites FinFET-a self-aligned double-gate MOSFET scalable to 20 nm.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning FinFET-a self-aligned double-gate MOSFET scalable to 20 nm

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.056386Z

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Observation 5858d73d-32d1-4bc8-a7e9-de44caa39c20 · outbound

This paper cites Stacked nanosheet gate-all-around transistor to enable scaling beyond FinFET.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Stacked nanosheet gate-all-around transistor to enable scaling beyond FinFET

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.062091Z

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Observation 65aca730-8687-48af-b5da-2481329a4c17 · outbound

This paper cites General relationship for the thermal oxidation of silicon.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning General relationship for the thermal oxidation of silicon

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.069617Z

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Observation 9029d072-77a1-491d-b337-e231e7d8d837 · outbound

This paper cites Kinetics of Thermal Growth of Ultra-Thin Layers of SiO2 on Silicon: Part II. Theory.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Kinetics of Thermal Growth of Ultra-Thin Layers of SiO2 on Silicon: Part II. Theory

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.043039Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 408f31d7-bfd7-4e5f-9a8f-3577cdfbc734 · outbound

This paper cites Thermal oxidation of silicon: In situ measurement of the growth rate using ellipsometry.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Thermal oxidation of silicon: In situ measurement of the growth rate using ellipsometry

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.046619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6a8b7546-3ead-4629-b95a-a72eb637a1a1 · outbound

This paper cites Thermal oxidation of silicon in dry oxygen: accurate determination of the kinetic rate constants.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Thermal oxidation of silicon in dry oxygen: accurate determination of the kinetic rate constants

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.039799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 37cdde88-72de-450a-b23e-7ea430a30835 · outbound

This paper cites Dynamic modeling of Si (100) thermal oxidation: Oxidation mechanisms and realistic amorphous interface generation.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Dynamic modeling of Si (100) thermal oxidation: Oxidation mechanisms and realistic amorphous interface generation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.099763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f9e96be3-6e4f-4d7e-b930-0f17c19273b3 · outbound

This paper cites Reactions and diffusion of water and oxygen molecules in amorphous SiO 2.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Reactions and diffusion of water and oxygen molecules in amorphous SiO 2

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.023820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9de4a52d-14fa-4fcd-a052-82e08b352e44 · outbound

This paper cites An 18O study of the thermal oxidation of silicon in oxygen.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning An 18O study of the thermal oxidation of silicon in oxygen

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.037371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 317442f6-ebf3-4444-85c1-3ba8122e96b4 · outbound

This paper cites An 18O study of the oxidation mechanism of silicon in dry oxygen.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning An 18O study of the oxidation mechanism of silicon in dry oxygen

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.108565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 17cc3eb1-269f-43cd-a7f8-e2d7941993dd · outbound

This paper cites Oxygen mobility in silicon dioxide and silicate glasses: a review.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Oxygen mobility in silicon dioxide and silicate glasses: a review

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.013353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d1970080-3dd6-46db-a8ef-eeb80f44db1d · outbound

This paper cites Multiscale modeling of oxygen diffusion through the oxide during silicon oxidation.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Multiscale modeling of oxygen diffusion through the oxide during silicon oxidation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.015887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a39e749e-27f4-4b77-8ef8-8dddfec40af8 · outbound

This paper cites Discovering catalytic reaction networks using deep reinforcement learning from first-principles.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Discovering catalytic reaction networks using deep reinforcement learning from first-principles

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.006231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 54d09e4c-ca14-48a8-9799-ddf11fa24c10 · outbound

This paper cites Molecular Autonomous Pathfinder Using Deep Reinforcement Learning.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Molecular Autonomous Pathfinder Using Deep Reinforcement Learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.117001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e973eaf9-463d-4cbe-a681-274595c12d06 · outbound

This paper cites Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.002910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7fbb61ca-6e61-42ce-820b-1f0e79b13bcd · outbound

This paper cites Reinforcement Learning-Guided Long-Timescale Simulation of Hydrogen Transport in Metals.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Reinforcement Learning-Guided Long-Timescale Simulation of Hydrogen Transport in Metals

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.008781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e5b92552-248b-46e4-9a0c-626dca5b9a2d · outbound

This paper cites Stridernet: A graph reinforcement learning approach to optimize atomic structures on rough energy landscapes.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Stridernet: A graph reinforcement learning approach to optimize atomic structures on rough energy landscapes

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.019213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9a5fa75d-b897-4818-9f0a-4940dbf2ec4b · outbound

This paper cites Learning with delayed rewards—a case study on inverse defect design in 2D materials.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Learning with delayed rewards—a case study on inverse defect design in 2D materials

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.049087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8fdb244a-74a8-4240-ad98-f6cbf547b5c7 · outbound

This paper cites A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.067095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b402337f-5d18-49bd-8d6b-67859980124d · outbound

This paper cites Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.132159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 08c7039a-3f41-4705-ab07-b6b9a6962d56 · outbound

This paper cites Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:39.141934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 95d762eb-7aad-4841-9884-1b41498ed537 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-05-20T16:28:38.103101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ce0ba1af-ec6a-440f-a400-987dafc26358 · outbound

This paper cites Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-20T16:28:38.113385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 12a20a60-928e-4451-a766-7562c42cdc86 · outbound

This paper cites LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.993540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fb28f89c-81a2-4ada-9e5e-3afe63c46b98 · outbound

This paper cites Development of the reactive force field and silicon dry/wet oxidation process modeling.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Development of the reactive force field and silicon dry/wet oxidation process modeling

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.999411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 61bfb0c7-a87f-4e9c-9bc0-0f3a105859d9 · outbound

This paper cites Machine learning force field for thermal oxidation of silicon.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Machine learning force field for thermal oxidation of silicon

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.983274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e1ae9b65-2f8b-4546-bf25-25c580f4a8bc · outbound

This paper cites Atom-centered symmetry functions for constructing high-dimensional neural network potentials.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Atom-centered symmetry functions for constructing high-dimensional neural network potentials

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.986430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4d274597-fc77-48d6-9050-d9b841afc7f7 · outbound

This paper cites Riemann manifold langevin and hamiltonian monte carlo methods.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Riemann manifold langevin and hamiltonian monte carlo methods

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.974723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2745887f-4628-48b4-a8aa-60efe5d95310 · outbound

This paper cites Neural spline flows.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Neural spline flows

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.970918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a645fe65-2f79-4af7-be94-1f96ecee3c30 · outbound

This paper cites Dispersion on a sphere.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Dispersion on a sphere

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.980470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 43099f4a-baa1-4ddd-961d-03e206a56f63 · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-05-20T16:28:38.106373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a7925d66-bdfb-41ff-96b1-b555fe71a96e · outbound

This paper cites In situ ESR observation of interface dangling bond formation processes during ultrathin SiO 2 growth on Si (111).

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning In situ ESR observation of interface dangling bond formation processes during ultrathin SiO 2 growth on Si (111)

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.990059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 29a47cf3-c724-4232-b211-83b14689ad55 · outbound

This paper cites Inherent Si dangling bond defects at the thermal (110) Si/SiO 2 interface.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Inherent Si dangling bond defects at the thermal (110) Si/SiO 2 interface

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.965690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3a518e2b-4f30-43f6-9d9c-3b9fad856388 · outbound

This paper cites NIST Chemistry WebBook, NIST Standard Reference Database 69.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning NIST Chemistry WebBook, NIST Standard Reference Database 69

Reference 71

Resolution: verified exact; doi, observed 2026-05-20T16:28:37.559499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d785ee13-9f56-476b-9054-0e62356e70b0 · outbound

This paper cites Vibrational thermodynamics of materials.

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning Vibrational thermodynamics of materials

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T16:28:38.960077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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