Wonyoung Choe (@chem571) 's Twitter Profile
Wonyoung Choe

@chem571

Professor | UNIST 🇰🇷 | Porous Materials | Digital Discovery for sustainable 🌏 #ChemicalSpaceExplorer | @ChoeGroup

ID: 609906622

calendar_today16-06-2012 11:07:29

700 Tweet

1,1K Followers

1,1K Following

Shao-Liang Zheng (@shaoliangzheng) 's Twitter Profile Photo

Fantastic work! 🎉 After identifying key #interactions, #CrystalStructures with known #elastic deformation trends can be extrapolated to pinpoint restoring force locations. 🏗️🔬nature.com/articles/s4156… Nature Materials Jack Clegg #crystallography #education

MOF Papers (@mof_papers) 's Twitter Profile Photo

Machine learning-assisted design of metal–organic frameworks for hydrogen storage: A high-throughput screening and experimental approach sciencedirect.com/science/articl…

MOF Papers (@mof_papers) 's Twitter Profile Photo

Engineering Photoswitching Dynamics in 3D Photochromic Metal–Organic Frameworks through a Metal–Organic Polyhedron Design dx.doi.org/10.1021/jacs.4…

COF_Papers (@cof_papers) 's Twitter Profile Photo

Gram-Scale Synthesis of Imine-Linked Covalent Organic Frameworks at Ambient Conditions Using Metal Triflimides dx.doi.org/10.1021/acs.ch…

COF_Papers (@cof_papers) 's Twitter Profile Photo

Reticular Design and Synthesis of Covalent Organic Frameworks with Irregular Hexagonal Tiling dx.doi.org/10.1021/jacs.5…

Machine Learning in Chemistry (@ml_chem) 's Twitter Profile Photo

Leveraging Prompt Engineering in Large Language Models for Accelerating Chemical Research #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

Cory Simon (@corymsimon) 's Twitter Profile Photo

🦨"Why an over-reliance on AI-driven modeling is bad for science" there's a rush to adopt AI (wonder what "AI's rise in research" looks like for chemistry & chemical engineering 👀). - easy for researchers to overestimate the predictive capabilities of an AI model, creating the

🦨"Why an over-reliance on AI-driven modeling is bad for science"

there's a rush to adopt AI (wonder what "AI's rise in research" looks like for chemistry & chemical engineering 👀).

- easy for researchers to overestimate the predictive capabilities of an AI model, creating the
MOF Papers (@mof_papers) 's Twitter Profile Photo

Discovering Ultra-Stable Metal–Organic Frameworks for CO2 Capture from A Wet Flue Gas: Integrating Machine Learning and Molecular Simulation dx.doi.org/10.1021/acs.es…

Karena Chapman (@kchaplab) 's Twitter Profile Photo

🐚ACC isn't just amorphous CaCO₃🫟!? Its missing CO₃²⁻—replaced by Cl⁻ & NO₃⁻🧪. And only cleans up its composition as it crystallizes✨ Read more in J. Am. Chem. Soc. with imaging📽️ & Xrays🔦🩻 StonyBrookChem and NMR🧲Iowa State Department of Chemistry @efrcgenesis pubs.acs.org/doi/10.1021/ja…

MOF Papers (@mof_papers) 's Twitter Profile Photo

Refining a Generic Force Field for Predicting Phase Transitions in Wine-Rack Metal–Organic Frameworks dx.doi.org/10.1021/acs.jc…

COF_Papers (@cof_papers) 's Twitter Profile Photo

An Ethynyl-Linked sp-Carbon-Conjugated Covalent Organic Framework through Sonogashira Cross-Coupling Reactions dx.doi.org/10.1021/jacs.5…

COF_Papers (@cof_papers) 's Twitter Profile Photo

Pore Engineering in Metal–organic Frameworks and Covalent Organic Frameworks: Strategies and Applications pubs.rsc.org/en/Content/Art…

novoMOF (@novomof) 's Twitter Profile Photo

🤖 MOFs meet #MachineLearning! MOFClassifier is a graph-based #AI tool that checks if MOF structures are computation-ready. 📈 With a stellar ROC of 0.979, it reduces false negatives & helps identify better #MOFs for real-world uses like #CarbonCapture. 🔗 hubs.li/Q03vBbK20

🤖 MOFs meet #MachineLearning! MOFClassifier is a graph-based #AI tool that checks if MOF structures are computation-ready. 📈 With a stellar ROC of 0.979, it reduces false negatives & helps identify better #MOFs for real-world uses like #CarbonCapture. 🔗 hubs.li/Q03vBbK20