MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow | Paper Citation Record

Observation 865fd0ce-64e4-41af-a405-d67b438e5d96 · outbound

This paper cites Climate impact of increasing atmospheric carbon dioxide,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Climate impact of increasing atmospheric carbon dioxide,

Reference 1

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Observation b16a9440-f582-482e-b864-f53576ff2590 · outbound

This paper cites Ultrahigh metal– organic framework loading and flexible nanofibrous membranes for efficient CO2 capture with long-term, ultrastable recyclability,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Ultrahigh metal– organic framework loading and flexible nanofibrous membranes for efficient CO2 capture with long-term, ultrastable recyclability,

Reference 2

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Observation 704cf429-1851-4d4e-b55b-d48c835a3fc4 · outbound

This paper cites Rapid and accurate machine learning recognition of high performing metal organic frameworks for CO2 capture,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Rapid and accurate machine learning recognition of high performing metal organic frameworks for CO2 capture,

Reference 3

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Observation c74b3766-274d-4da9-8bff-bca680c47daf · outbound

This paper cites Recent ad- vances in gas storage and separation using metal–organic frameworks,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Recent ad- vances in gas storage and separation using metal–organic frameworks,

Reference 4

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This paper cites Recent advances on preparation and environmental applications of MOF-derived carbons in catalysis,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Recent advances on preparation and environmental applications of MOF-derived carbons in catalysis,

Reference 5

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Observation 9f8d649d-b4ad-40d2-bfba-0311f999cf86 · outbound

This paper cites Metal–organic frameworks for drug delivery: A design perspective,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Metal–organic frameworks for drug delivery: A design perspective,

Reference 6

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Observation 53bede35-180d-4e4a-8d93-20c93c78df45 · outbound

This paper cites Lu- minescent sensors based on metal-organic frameworks,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Lu- minescent sensors based on metal-organic frameworks,

Reference 7

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Observation 5e2d1d76-5e07-4954-912f-7cc44b9bf358 · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow High- resolution image synthesis with latent diffusion models,

Reference 8

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Observation be86b009-bddf-4c0e-bf40-14977fca9a1d · outbound

This paper cites Big-data science in porous materials: Materials genomics and machine learning,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Big-data science in porous materials: Materials genomics and machine learning,

Reference 9

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Observation 5b2e9ae9-67fb-44c2-84fa-d642f173a88b · outbound

This paper cites ChatMOF: An artificial intelligence system for predicting and generating metal-organic frameworks using large language models,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow ChatMOF: An artificial intelligence system for predicting and generating metal-organic frameworks using large language models,

Reference 10

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Observation 10b9dc00-f848-4c37-837e-0e26a371ace5 · outbound

This paper cites A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture,

Reference 11

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Observation 22c2d770-79b4-40e8-a7fe-c5b84d9eaad4 · outbound

This paper cites Understanding the diversity of the metal-organic framework ecosystem,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Understanding the diversity of the metal-organic framework ecosystem,

Reference 12

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Observation 71cc9410-c1b4-404e-b10c-3a8171249567 · outbound

This paper cites CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations,

Reference 13

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Observation 7c5a1aca-c748-47e5-b927-d8ee2c2ab50f · outbound

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

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow LAMMPS - A flexible simulation tool for particle- based materials modeling at the atomic, meso, and continuum scales,

Reference 14

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Observation 434ab342-0178-4c70-80a1-bf7f9ee8ee2e · outbound

This paper cites RASPA: Molecular simulation software for adsorption and diffusion in flexible nanoporous materials,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow RASPA: Molecular simulation software for adsorption and diffusion in flexible nanoporous materials,

Reference 15

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Observation 604682eb-3303-4395-98b7-e6eda5093ccd · outbound

This paper cites Parsl: Pervasive Parallel Programming in Python,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Parsl: Pervasive Parallel Programming in Python,

Reference 16

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Observation 80663b90-4b98-4904-9c70-37a417c6d84d · outbound

This paper cites Colmena: Scalable machine-learning-based steering of ensemble sim- ulations for high performance computing,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Colmena: Scalable machine-learning-based steering of ensemble sim- ulations for high performance computing,

Reference 17

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Observation ecb81e46-127a-4efc-874d-7454655a3e2d · outbound

This paper cites Structure–property relationships of porous materials for carbon dioxide separation and capture,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Structure–property relationships of porous materials for carbon dioxide separation and capture,

Reference 18

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Observation 4bee5dfd-833c-48f1-a9ec-20b454d7c108 · outbound

This paper cites State of the art and prospects in metal–organic framework (MOF)-based and MOF-derived nanocatalysis,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow State of the art and prospects in metal–organic framework (MOF)-based and MOF-derived nanocatalysis,

Reference 19

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Observation be742841-a329-44ef-a025-67581c081f19 · outbound

This paper cites Stability of metal-organic frameworks: Recent advances and future trends,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Stability of metal-organic frameworks: Recent advances and future trends,

Reference 20

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Observation e9a76340-4046-40b1-aa6c-09ce5e91c728 · outbound

This paper cites Preparation, clathration ability, and catalysis of a two-dimensional square network material composed of cadmium (II) and 4, 4’-bipyridine,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Preparation, clathration ability, and catalysis of a two-dimensional square network material composed of cadmium (II) and 4, 4’-bipyridine,

Reference 21

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Observation d00a23b0-003d-437a-a2d8-deaa7af45dba · outbound

This paper cites Engineering metal organic frameworks for heterogeneous catalysis,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Engineering metal organic frameworks for heterogeneous catalysis,

Reference 22

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Observation 328b01d6-c12a-4d37-b845-9f8baa8224c4 · outbound

This paper cites Metal–organic frameworks meet metal nanoparticles: Synergistic effect for enhanced catalysis,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Metal–organic frameworks meet metal nanoparticles: Synergistic effect for enhanced catalysis,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.847223Z

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Observation ea49a7fd-ad21-49df-b49a-3b1673d099a3 · outbound

This paper cites Metal–organic framework-derived porous materials for catalysis,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Metal–organic framework-derived porous materials for catalysis,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.835396Z

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Observation 3f81b09c-f338-465c-8df6-fe54c78a8938 · outbound

This paper cites Large-scale screening of hypothetical metal–organic frameworks,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Large-scale screening of hypothetical metal–organic frameworks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.824091Z

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Observation b6b6dfa5-1fd6-42f9-a704-f9db9719de80 · outbound

This paper cites Computational screening of metal–organic frameworks for membrane-based CO2/N2/H2O separations: Best ma- terials for flue gas separation,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Computational screening of metal–organic frameworks for membrane-based CO2/N2/H2O separations: Best ma- terials for flue gas separation,

Reference 26

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Observation ebe73a87-6aee-499c-88f8-32ffd0283dd7 · outbound

This paper cites Geometrical properties can predict CO2 and N2 adsorption performance of metal–organic frameworks (MOFs) at low pressure,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Geometrical properties can predict CO2 and N2 adsorption performance of metal–organic frameworks (MOFs) at low pressure,

Reference 27

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Observation fb055edb-41db-4a0e-9d63-6d1813b0a412 · outbound

This paper cites Sequential design of adsorption simulations in metal–organic frameworks,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Sequential design of adsorption simulations in metal–organic frameworks,

Reference 28

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Observation 178ece6a-02a2-49fd-a7c5-c3a719e52842 · outbound

This paper cites Generative AI for designing and validating easily synthesizable and structurally novel antibiotics,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Generative AI for designing and validating easily synthesizable and structurally novel antibiotics,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.768565Z

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Observation ef2d570e-8d57-4f6c-940c-fac6bc2e6864 · outbound

This paper cites Efficient aerodynamic shape optimization with deep-learning-based geometric filtering,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Efficient aerodynamic shape optimization with deep-learning-based geometric filtering,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.752523Z

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Observation e13b93be-130b-4950-82f4-d275be45d069 · outbound

This paper cites Virtual screening of inorganic materials synthesis parameters with deep learning,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Virtual screening of inorganic materials synthesis parameters with deep learning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.738950Z

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Observation b1d7b5b4-ab33-4f06-be22-ae73e7feee7d · outbound

This paper cites Equivariant 3D-conditional diffusion models for molecular linker design,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Equivariant 3D-conditional diffusion models for molecular linker design,

Reference 32

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Observation 86079a73-2071-4fd3-948c-16beb3904281 · outbound

This paper cites MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design

Reference 33

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Observation d3731cce-ba68-4932-abbd-f5cf2ed5d499 · outbound

This paper cites Inverse design of nanoporous crystalline reticular materials with deep generative models,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Inverse design of nanoporous crystalline reticular materials with deep generative models,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.713112Z

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Observation 41e97b77-4b01-441e-a519-7c92f5f01683 · outbound

This paper cites Learning everywhere: A taxonomy for the in- tegration of machine learning and simulations,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Learning everywhere: A taxonomy for the in- tegration of machine learning and simulations,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.701270Z

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Observation cddaad7c-bda9-4e22-85e8-98391cea0314 · outbound

This paper cites Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster,

Reference 36

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Observation 017c7fa9-014f-4374-a6f3-d916ca09a5dd · outbound

This paper cites Dask: Parallel computation with blocked algorithms and task scheduling,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Dask: Parallel computation with blocked algorithms and task scheduling,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.671351Z

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Observation 9df54784-3eb3-4171-b8ba-c3464ff376ad · outbound

This paper cites FireWorks: A dynamic workflow system designed for high-throughput applications,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow FireWorks: A dynamic workflow system designed for high-throughput applications,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.659888Z

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Observation 50c63fbd-ac2b-4e7a-beb1-06f9f2ced4d7 · outbound

This paper cites Pegasus, a workflow management system for science automation,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Pegasus, a workflow management system for science automation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.648586Z

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Observation 1767d941-3cb3-4efc-a562-cffeb53491df · outbound

This paper cites Swift: A language for distributed parallel scripting,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Swift: A language for distributed parallel scripting,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.637569Z

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Observation 2fa4ca30-5a7a-46cd-862c-9a3339b551af · outbound

This paper cites Ray: A dis- tributed framework for emerging AI applications,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Ray: A dis- tributed framework for emerging AI applications,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.626694Z

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Observation bdb56389-13ad-4172-8c53-31aa4e0971c7 · outbound

This paper cites TaskVine: Managing in-cluster storage for high-throughput data intensive workflows,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow TaskVine: Managing in-cluster storage for high-throughput data intensive workflows,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.616122Z

Source-reported events for the cited work

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Observation 7d431ec4-2d00-4de9-aa1c-9b4bd5a7e7cc · outbound

This paper cites AWS Lambda.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow AWS Lambda

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.604226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation af2aebac-97c1-401d-8650-eb37f9c66bed · outbound

This paper cites Serverless execution of scientific workflows: Experiments with Hyperflow, AWS Lambda and Google Cloud Functions,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Serverless execution of scientific workflows: Experiments with Hyperflow, AWS Lambda and Google Cloud Functions,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.592417Z

Source-reported events for the cited work

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Observation fccd718f-d469-4a16-9ab5-e79e6c417a5e · outbound

This paper cites FuncX: A federated function serving fabric for science,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow FuncX: A federated function serving fabric for science,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.579487Z

Source-reported events for the cited work

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Observation a6b398d7-2264-44a8-b3a0-deaebdb96c41 · outbound

This paper cites Exaworks: Workflows for exascale,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Exaworks: Workflows for exascale,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.567802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 28afd599-8e08-4977-8d4d-9019ce081e9f · outbound

This paper cites High throughput training of deep surrogates from large ensemble runs,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow High throughput training of deep surrogates from large ensemble runs,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.555672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8f67ef01-0271-47b4-a6a6-ad2fee014152 · outbound

This paper cites GenSLMs: Genome- scale language models reveal SARS-CoV-2 evolutionary dynamics,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow GenSLMs: Genome- scale language models reveal SARS-CoV-2 evolutionary dynamics,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.541875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 21be03b8-4712-406e-87d0-5380119791ac · outbound

This paper cites Composition-transferable machine learning potential for LiCl-KCl molten salts validated by high-energy X- ray diffraction,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Composition-transferable machine learning potential for LiCl-KCl molten salts validated by high-energy X- ray diffraction,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.528614Z

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Observation 44402cde-ca5c-492f-b8ff-feccc2c980c9 · outbound

This paper cites Extreme scale survey simulation with Python workflows,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Extreme scale survey simulation with Python workflows,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.514185Z

Source-reported events for the cited work

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Observation d4fba1bf-fd29-495b-9659-feabdf7276bc · outbound

This paper cites Inverse design of materials by multi-objective differential evolution,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Inverse design of materials by multi-objective differential evolution,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.499141Z

Source-reported events for the cited work

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Observation 574b0d24-94af-43c6-b51e-fa5937093253 · outbound

This paper cites Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.483895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b1f24cd6-1a17-4877-a7be-cf228d44c764 · outbound

This paper cites Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal struc- tures,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal struc- tures,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.468948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 76442a3c-a3f1-4f5f-b3c0-96fed4743dfa · outbound

This paper cites Inverse design of porous materials using artificial neural networks,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Inverse design of porous materials using artificial neural networks,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.454902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 042c951d-04bb-41f2-9966-4982093ba790 · outbound

This paper cites Optimal experimental design: Formulations and computations,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Optimal experimental design: Formulations and computations,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.440142Z

Source-reported events for the cited work

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Observation ba983c84-e8c2-4e6c-93e7-a22c2c88f914 · outbound

This paper cites E(n) equivariant graph neural networks,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow E(n) equivariant graph neural networks,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.423727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ee7bbe9d-8efb-460b-8efd-65d5b24fb3fb · outbound

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

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.410364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f057ad44-93b5-411e-a9e0-8fd2cdcc2767 · outbound

This paper cites GEOM, energy-annotated molecular conformations for property prediction and molecular genera- tion,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow GEOM, energy-annotated molecular conformations for property prediction and molecular genera- tion,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.395909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ee1d212a-16d9-48b1-8728-f301a6f711a1 · outbound

This paper cites Open Babel: An open chemical toolbox,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Open Babel: An open chemical toolbox,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.381051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f49ca455-713d-4738-ab2a-7143c82fb6e3 · outbound

This paper cites Merck molecular force field. II. MMFF94 van der Waals and electrostatic parameters for intermolecular interactions,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Merck molecular force field. II. MMFF94 van der Waals and electrostatic parameters for intermolecular interactions,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.364259Z

Source-reported events for the cited work

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Observation 160fc490-f662-4f6b-a270-0504c678769e · outbound

This paper cites Rdkit documentation,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Rdkit documentation,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-10T19:06:09.832661Z

Source-reported events for the cited work

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Observation e0960746-fe45-46f3-9987-77032d4249a6 · outbound

This paper cites The Reticular Chemistry Structure Resource (RCSR) Database of, and Symbols for, Crystal Nets,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow The Reticular Chemistry Structure Resource (RCSR) Database of, and Symbols for, Crystal Nets,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.340175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 31fe7968-f422-425d-a022-4015d40c63db · outbound

This paper cites OChemDb: The free on-line Open Chemistry Database portal for searching and analysing crystal structure information,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow OChemDb: The free on-line Open Chemistry Database portal for searching and analysing crystal structure information,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.328342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7da264b4-9258-4169-8f88-c5b8b46279b9 · outbound

This paper cites cif2lammps.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow cif2lammps

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.316508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e137d280-1f6e-42be-b7c8-801ad3a76340 · outbound

This paper cites Extension of the universal force field to metal–organic frameworks,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Extension of the universal force field to metal–organic frameworks,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.304009Z

Source-reported events for the cited work

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Observation 5487a8ad-8da8-425d-8332-498d687da82f · outbound

This paper cites Extension of the universal force field for metal–organic frameworks,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Extension of the universal force field for metal–organic frameworks,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.281734Z

Source-reported events for the cited work

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Observation 1c64a826-9847-439b-beb0-df22c19b80db · outbound

This paper cites Quickstep: Fast and accurate density functional calcu- lations using a mixed Gaussian and plane waves approach,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Quickstep: Fast and accurate density functional calcu- lations using a mixed Gaussian and plane waves approach,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.266042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7b826526-f9b9-4f33-a812-ae05717d63fe · outbound

This paper cites On the limited memory BFGS method for large scale optimization,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow On the limited memory BFGS method for large scale optimization,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.252242Z

Source-reported events for the cited work

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Observation 9ccc96df-072d-4519-a006-0bacb2e8852e · outbound

This paper cites Generalized gradient approximation made simple,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Generalized gradient approximation made simple,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.239982Z

Source-reported events for the cited work

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Observation aaa91884-1fc9-4278-8e01-f7a0fe3e98bf · outbound

This paper cites Separable dual-space Gaussian pseudopotentials,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Separable dual-space Gaussian pseudopotentials,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.222551Z

Source-reported events for the cited work

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Observation eb60a5cd-f17f-4e02-bc15-5fa4deee79df · outbound

This paper cites Gaussian basis sets for accurate calcu- lations on molecular systems in gas and condensed phases,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Gaussian basis sets for accurate calcu- lations on molecular systems in gas and condensed phases,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.210083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 776cd951-5f37-451a-b73c-ecc667fd9a69 · outbound

This paper cites A consistent and accu- rateab initioparametrization of density functional dispersion correction (dft-d) for the 94 elements h-pu,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow A consistent and accu- rateab initioparametrization of density functional dispersion correction (dft-d) for the 94 elements h-pu,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.198466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 93e17a7a-829c-4175-ada8-b7496f8c3c99 · outbound

This paper cites Introducing DDEC6 atomic population analysis: Part 1. Charge partitioning theory and methodology,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Introducing DDEC6 atomic population analysis: Part 1. Charge partitioning theory and methodology,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.186141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d733c785-1f51-4104-90c9-5833e119c44f · outbound

This paper cites Introducing DDEC6 atomic population analysis: Part 2. Computed results for a wide range of periodic and nonperiodic materials,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Introducing DDEC6 atomic population analysis: Part 2. Computed results for a wide range of periodic and nonperiodic materials,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.172509Z

Source-reported events for the cited work

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Observation 363ffc3c-263c-4d1e-8c46-a58636b4cb72 · outbound

This paper cites Cloud services enable efficient AI-guided simulation workflows across heterogeneous resources,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Cloud services enable efficient AI-guided simulation workflows across heterogeneous resources,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.158477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 55645ecf-8c4a-4a37-b702-f14355f73317 · outbound

This paper cites Employing artificial intelli- gence to steer exascale workflows with Colmena,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Employing artificial intelli- gence to steer exascale workflows with Colmena,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.146012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4b10a1ed-3f5e-4bb0-9299-d9d6a098339f · outbound

This paper cites Accelerating communications in federated applications with transparent object proxies,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Accelerating communications in federated applications with transparent object proxies,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.132836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3591536f-3e65-4acb-a1d0-4d1669500deb · outbound

This paper cites Object Proxy Patterns for Accelerating Distributed Applications.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Object Proxy Patterns for Accelerating Distributed Applications

Reference 78

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:06:09.994590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7b28dae0-77b0-4159-9831-008b07b53634 · outbound

This paper cites NVIDIA Multi Process Service.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow NVIDIA Multi Process Service

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.119204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4c9eebdb-1643-4be9-98ee-b78bb755f531 · outbound

This paper cites CD-MOFs for CO2 capture and sep- aration: Current research and future outlook,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow CD-MOFs for CO2 capture and sep- aration: Current research and future outlook,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.103054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0d1040c4-ed51-4401-8ff5-d4e597209d5b · outbound

This paper cites Rational design of a low-cost, high-performance metal-organic framework for hydrogen storage and carbon capture,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Rational design of a low-cost, high-performance metal-organic framework for hydrogen storage and carbon capture,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.084311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b96bbb1e-797b-48fb-bfc0-bc3bbb926c1e · outbound

This paper cites Chapter 5 - Removal of toxic/radioactive metal ions by metal-organic framework-based materials,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Chapter 5 - Removal of toxic/radioactive metal ions by metal-organic framework-based materials,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.065898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b27a61d6-16bf-4f54-94ad-eebf7b4e41d3 · outbound

This paper cites A review on metal-organic frameworks: Synthesis and applications,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow A review on metal-organic frameworks: Synthesis and applications,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.048976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5e73aba8-10c5-4f39-854f-967449e031ed · outbound

This paper cites Advances and applications of metal-organic frameworks (MOFs) in emerging technologies: A comprehensive review,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow Advances and applications of metal-organic frameworks (MOFs) in emerging technologies: A comprehensive review,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.035595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b32bfed0-11aa-499e-b8ec-a45cecd1aea9 · outbound

This paper cites The present state and challenges of active learning in drug discovery,.

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow The present state and challenges of active learning in drug discovery,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:10.021988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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