Research graph
References from Synthesizability: The Open Question in Digital MOF Discovery. Local targets link to admitted publications; unresolved targets remain external evidence.
Large-scale screening of hypothetical metal–organic frameworks
10.1038/nchem.1192 · 2011 · External reference
AuToGraFS: Automatic Topological Generator for Framework Structures
10.1021/jp507643v · 2014 · External reference
Topologically Guided, Automated Construction of Metal–Organic Frameworks and Their Evaluation for Energy-Related Applications
10.1021/acs.cgd.7b00848 · 2017 · External reference
Increasing topological diversity during computational “synthesis” of porous crystals: how and why
10.1039/c8ce01637b · 2019 · External reference
Generative AI for crystal structures: a review
10.1038/s41524-025-01881-2 · 2025 · External reference
The rise of generative AI for metal-organic framework design and synthesis
10.1016/j.matt.2026.102748 · 2026 · External reference
High-throughput computational screening of nanoporous materials in targeted applications
10.1039/d2dd00018k · 2022 · External reference
MOSAEC-DB: a comprehensive database of experimental metal–organic frameworks with verified chemical accuracy suitable for molecular simulations
10.1039/d4sc07438f · 2025 · External reference
Free energy predictions for crystal stability and synthesisability
10.1039/d2dd00050d · 2022 · External reference
Interrogating the synthetic likelihood of metal–organic frameworks: a digital discovery perspective
10.1039/d6sc02765b · 2026 · External reference
Experimental and Theoretical Evaluation of the Stability of True MOF Polymorphs Explains Their Mechanochemical Interconversions
10.1021/jacs.7b03144 · 2017 · External reference
Thermodynamic and Kinetic Effects in the Crystallization of Metal–Organic Frameworks
10.1021/acs.accounts.7b00497 · 2018 · External reference
Large-Scale Free Energy Calculations on a Computational Metal–Organic Frameworks Database: Toward Synthetic Likelihood Predictions
10.1021/acs.chemmater.0c00744 · 2020 · External reference
MOFSynth: A Computational Tool toward Synthetic Likelihood Predictions of MOFs
10.1021/acs.jcim.4c01298 · 2024 · External reference
Digital Discovery of Synthesizable Metal–Organic Frameworks via Molecular Dynamics-Informed, High-Fidelity Deep Learning
10.1002/adfm.202519565 · 2025 · External reference
Accurate predictions on small data with a tabular foundation model
10.1038/s41586-024-08328-6 · 2025 · External reference
Highly Accurate and Fast Prediction of MOF Free Energy via Machine Learning
10.1021/jacs.5c13960 · 2025 · External reference
Predicting the Thermodynamic Limits of Metal–Organic Framework Metastability
10.1021/jacs.5c20253 · 2026 · External reference
Hybrid Perovskites, Metal–Organic Frameworks, and Beyond: Unconventional Degrees of Freedom in Molecular Frameworks
10.1021/acs.accounts.0c00797 · 2021 · External reference
Review of computational approaches to predict the thermodynamic stability of inorganic solids
10.1007/s10853-022-06915-4 · 2022 · External reference
The thermodynamic scale of inorganic crystalline metastability
10.1126/sciadv.1600225 · 2016 · External reference
Accelerated data-driven materials science with the Materials Project
10.1038/s41563-025-02272-0 · 2025 · External reference
Kinetic stability of metal–organic frameworks for corrosive and coordinating gas capture
10.1038/s41578-019-0140-1 · 2019 · External reference
Identification of New Templates for the Synthesis of BEA, BEC, and ISV Zeolites Using Molecular Topology and Monte Carlo Techniques
10.1021/acs.jcim.0c00231 · 2020 · External reference
A priori control of zeolite phase competition and intergrowth with high-throughput simulations
10.1126/science.abh3350 · 2021 · External reference
Interactions of Common Synthesis Solvents with MOFs Studied via Free Energies of Solvation: Implications on Stability and Polymorph Selection
10.1021/acs.chemmater.5c01410 · 2026 · External reference
MOFSynth-ADV: An Open-Source Engine for Synthesizability Evaluation of Metal–Organic Frameworks
10.1021/acs.jcim.6c00880 · 2026 · External reference
Extended tight-binding quantum chemistry methods
10.1002/wcms.1493 · 2020 · External reference
A practical guide to machine learning interatomic potentials – Status and future
10.1016/j.cossms.2025.101214 · 2025 · External reference
Systematic investigation of the mechanical properties of pure silica zeolites: stiffness, anisotropy, and negative linear compressibility
10.1039/c3cp51817e · 2013 · External reference
Multicomponent Metal–Organic Frameworks as Defect-Tolerant Materials
10.1021/acs.chemmater.5b04306 · 2015 · External reference
Nanoscale metamaterials: Meta-MOFs and framework materials with anomalous behavior
10.1016/j.ccr.2019.02.023 · 2019 · External reference
Reliably Modeling the Mechanical Stability of Rigid and Flexible Metal–Organic Frameworks
10.1021/acs.accounts.7b00404 · 2017 · External reference
Integrating stability metrics with high-throughput computational screening of metal–organic frameworks for CO2 capture
10.1038/s43246-023-00409-9 · 2023 · External reference
Structure-Mechanical Stability Relations of Metal-Organic Frameworks via Machine Learning
10.1016/j.matt.2019.03.002 · 2019 · External reference
Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning
10.1021/acs.chemmater.0c00434 · 2020 · External reference
Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks
10.1021/jacsau.5c00077 · 2025 · External reference
Ranking the synthesizability of hypothetical zeolites with the sorting hat
10.1039/d2dd00056c · 2022 · External reference
A database of new zeolite-like materials
10.1039/c0cp02255a · 2011 · External reference
On representing chemical environments
10.1103/physrevb.87.184115 · 2013 · External reference
CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening
10.1016/j.matt.2025.102140 · 2025 · External reference
Accelerating metal–organic framework discovery via synthesisability prediction: the MFD evaluation method for one-class classification models
10.1039/d4dd00161c · 2024 · External reference
MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning
10.1002/anie.202200242 · 2022 · External reference
DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining
10.1021/acs.chemmater.3c00788 · 2023 · External reference
MOF-ChemUnity: Literature-Informed Large Language Models for Metal–Organic Framework Research
10.1021/jacs.5c11789 · 2025 · External reference
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks
10.1021/jacs.1c07217 · 2021 · External reference
Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set
10.1021/jacs.4c05879 · 2024 · External reference
Digital Discovery of Synthesizable Metal–Organic Frameworks via Molecular Dynamics-Informed, High-Fidelity Deep Learning
10.1002/adfm.202519565 · ExternalCitation · doi-reference
MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning
10.1002/anie.202200242 · ExternalCitation · doi-reference
Extended tight-binding quantum chemistry methods
10.1002/wcms.1493 · ExternalCitation · doi-reference
Review of computational approaches to predict the thermodynamic stability of inorganic solids
10.1007/s10853-022-06915-4 · ExternalCitation · doi-reference
Nanoscale metamaterials: Meta-MOFs and framework materials with anomalous behavior
10.1016/j.ccr.2019.02.023 · ExternalCitation · doi-reference
A practical guide to machine learning interatomic potentials – Status and future
10.1016/j.cossms.2025.101214 · ExternalCitation · doi-reference
Structure-Mechanical Stability Relations of Metal-Organic Frameworks via Machine Learning
10.1016/j.matt.2019.03.002 · ExternalCitation · doi-reference
CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening
10.1016/j.matt.2025.102140 · ExternalCitation · doi-reference
The rise of generative AI for metal-organic framework design and synthesis
10.1016/j.matt.2026.102748 · ExternalCitation · doi-reference
Hybrid Perovskites, Metal–Organic Frameworks, and Beyond: Unconventional Degrees of Freedom in Molecular Frameworks
10.1021/acs.accounts.0c00797 · ExternalCitation · doi-reference
Reliably Modeling the Mechanical Stability of Rigid and Flexible Metal–Organic Frameworks
10.1021/acs.accounts.7b00404 · ExternalCitation · doi-reference
Thermodynamic and Kinetic Effects in the Crystallization of Metal–Organic Frameworks
10.1021/acs.accounts.7b00497 · ExternalCitation · doi-reference
Topologically Guided, Automated Construction of Metal–Organic Frameworks and Their Evaluation for Energy-Related Applications
10.1021/acs.cgd.7b00848 · ExternalCitation · doi-reference
Speeding Up Discovery of Auxetic Zeolite Frameworks by Machine Learning
10.1021/acs.chemmater.0c00434 · ExternalCitation · doi-reference
Large-Scale Free Energy Calculations on a Computational Metal–Organic Frameworks Database: Toward Synthetic Likelihood Predictions
10.1021/acs.chemmater.0c00744 · ExternalCitation · doi-reference
DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining
10.1021/acs.chemmater.3c00788 · ExternalCitation · doi-reference
Multicomponent Metal–Organic Frameworks as Defect-Tolerant Materials
10.1021/acs.chemmater.5b04306 · ExternalCitation · doi-reference
Interactions of Common Synthesis Solvents with MOFs Studied via Free Energies of Solvation: Implications on Stability and Polymorph Selection
10.1021/acs.chemmater.5c01410 · ExternalCitation · doi-reference
Identification of New Templates for the Synthesis of BEA, BEC, and ISV Zeolites Using Molecular Topology and Monte Carlo Techniques
10.1021/acs.jcim.0c00231 · ExternalCitation · doi-reference
MOFSynth: A Computational Tool toward Synthetic Likelihood Predictions of MOFs
10.1021/acs.jcim.4c01298 · ExternalCitation · doi-reference
MOFSynth-ADV: An Open-Source Engine for Synthesizability Evaluation of Metal–Organic Frameworks
10.1021/acs.jcim.6c00880 · ExternalCitation · doi-reference
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks
10.1021/jacs.1c07217 · ExternalCitation · doi-reference
Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set
10.1021/jacs.4c05879 · ExternalCitation · doi-reference
MOF-ChemUnity: Literature-Informed Large Language Models for Metal–Organic Framework Research
10.1021/jacs.5c11789 · ExternalCitation · doi-reference
Highly Accurate and Fast Prediction of MOF Free Energy via Machine Learning
10.1021/jacs.5c13960 · ExternalCitation · doi-reference
Predicting the Thermodynamic Limits of Metal–Organic Framework Metastability
10.1021/jacs.5c20253 · ExternalCitation · doi-reference
Experimental and Theoretical Evaluation of the Stability of True MOF Polymorphs Explains Their Mechanochemical Interconversions
10.1021/jacs.7b03144 · ExternalCitation · doi-reference
Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks
10.1021/jacsau.5c00077 · ExternalCitation · doi-reference
AuToGraFS: Automatic Topological Generator for Framework Structures
10.1021/jp507643v · ExternalCitation · doi-reference
Large-scale screening of hypothetical metal–organic frameworks
10.1038/nchem.1192 · ExternalCitation · doi-reference
Generative AI for crystal structures: a review
10.1038/s41524-025-01881-2 · ExternalCitation · doi-reference
Accelerated data-driven materials science with the Materials Project
10.1038/s41563-025-02272-0 · ExternalCitation · doi-reference
Kinetic stability of metal–organic frameworks for corrosive and coordinating gas capture
10.1038/s41578-019-0140-1 · ExternalCitation · doi-reference
Accurate predictions on small data with a tabular foundation model
10.1038/s41586-024-08328-6 · ExternalCitation · doi-reference
Integrating stability metrics with high-throughput computational screening of metal–organic frameworks for CO2 capture
10.1038/s43246-023-00409-9 · ExternalCitation · doi-reference
A database of new zeolite-like materials
10.1039/c0cp02255a · ExternalCitation · doi-reference
Systematic investigation of the mechanical properties of pure silica zeolites: stiffness, anisotropy, and negative linear compressibility
10.1039/c3cp51817e · ExternalCitation · doi-reference
Increasing topological diversity during computational “synthesis” of porous crystals: how and why
10.1039/c8ce01637b · ExternalCitation · doi-reference
High-throughput computational screening of nanoporous materials in targeted applications
10.1039/d2dd00018k · ExternalCitation · doi-reference
Free energy predictions for crystal stability and synthesisability
10.1039/d2dd00050d · ExternalCitation · doi-reference
Ranking the synthesizability of hypothetical zeolites with the sorting hat
10.1039/d2dd00056c · ExternalCitation · doi-reference
Accelerating metal–organic framework discovery via synthesisability prediction: the MFD evaluation method for one-class classification models
10.1039/d4dd00161c · ExternalCitation · doi-reference
MOSAEC-DB: a comprehensive database of experimental metal–organic frameworks with verified chemical accuracy suitable for molecular simulations
10.1039/d4sc07438f · ExternalCitation · doi-reference
Interrogating the synthetic likelihood of metal–organic frameworks: a digital discovery perspective
10.1039/d6sc02765b · ExternalCitation · doi-reference
On representing chemical environments
10.1103/physrevb.87.184115 · ExternalCitation · doi-reference
The thermodynamic scale of inorganic crystalline metastability
10.1126/sciadv.1600225 · ExternalCitation · doi-reference
A priori control of zeolite phase competition and intergrowth with high-throughput simulations
10.1126/science.abh3350 · ExternalCitation · doi-reference
