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Data-Driven Organic Materials Lab

@ddomlab.org
195 followers 36 following 12 posts

Seifrid research group in MSE at NC State ddomlab.org

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Data-Driven Organic Materials Lab @ddomlab.org · 27/02/2026
We think there are important implications for anyone using ML to guide #polymer design or build a #SelfDrivingLab.
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Data-Driven Organic Materials Lab @ddomlab.org · 27/02/2026
Good in-distribution performance is the easy part. The harder question is when and why models fail on new polymers and formulations, and the answer isn't as simple as "more data."
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Data-Driven Organic Materials Lab @ddomlab.org · 27/02/2026
Can ML predict how conjugated polymers behave in solution, and will those models hold up on molecular structures they've never seen? We built a dataset and put them to the test. The results are more nuanced than we expected.
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Data-Driven Organic Materials Lab @ddomlab.org · 27/02/2026
Officially official! The group's first-ever publication is online 🥳 #ChemSky #MatSky doi.org/10.1063/5.03...
doi.org
Robust learning from literature data: Model generalizability and uncertainty for predicting conjugated polymer solution conformation
Predicting solution conformation and aggregation of conjugated polymers remains a bottleneck for translating solution processing into controlled film microstruc
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Reposted by Data-Driven Organic Materials Lab
Andy Extance @andyextance.bsky.social · 02/02/2026
In my @nature.com story, I explain how chemists at Yale have created a chemistry AI tool by fine-turning the LLAMA LLM to create 2,498 expert models. Their system can judge which of the different experts to refer a query about reaction conditions to. Impressive! 🧪 www.nature.com/articles/d41...
nature.com
This AI has chemical expertise — and helps synthesize 35 new compounds
An open-source program helps researchers bypass a major bottleneck in the process chemical synthesis.
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Data-Driven Organic Materials Lab @ddomlab.org · 01/10/2025
We explore how model performance can be assessed in this scenario, and use conjugated polymer conformation as a test case.
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Data-Driven Organic Materials Lab @ddomlab.org · 01/10/2025
Conventional or #selfdrivinglab experiments can be informed by prior data gathered from the literature. Important scientific challenges often require the development of previously unknown materials: materials discovery. However, #machinelearning models are not designed for this scenario...
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Data-Driven Organic Materials Lab @ddomlab.org · 01/10/2025
🚨🚨 The group's first preprint is up on @chemrxiv.org! 🥳🥳 Robust Learning from Literature Data: Model Generalizability and Uncertainty for Predicting Conjugated Polymer Solution Conformation 📝 doi.org/10.26434/chemrxiv-2025-mdtsm #chemsky #matsky
Robust Learning from Literature Data: Model Generalizability and Uncertainty for Predicting Conjugated Polymer Solution Conformation
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Reposted by Data-Driven Organic Materials Lab
Martin Seifrid @mseifrid.bsky.social · 18/09/2025
Do you know of any papers that have datasets of organic materials (polymers, molecules) with experimental parameters–not material properties, but stuff like concentration, temperature, whatever? #chemsky #matsky
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Reposted by Data-Driven Organic Materials Lab
Research Corporation for Science Advancement @rescorp.org · 11/06/2025
@uw-cei.bsky.social @imod-stc.bsky.social @coschoolofmines.bsky.social @utahchemistry.bsky.social @whitmancollege.bsky.social @ddomlab.org
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Data-Driven Organic Materials Lab @ddomlab.org · 21/06/2024
Another instrument is up and running! We set up our Gyros PurePep Chorus last Friday. Keep an eye out for exciting developments on this front 👀 #chemsky #matsky
Gyros PurePep chorus recently installed in the Data-Driven Organic Materials Lab @ NC State
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Data-Driven Organic Materials Lab @ddomlab.org · 06/06/2024
#chemsky #matsky
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Data-Driven Organic Materials Lab @ddomlab.org · 06/06/2024
We just completed the installation of Big Purchase #1 ™️: a quadruple (!) detector SEC from Tosoh. This bad boy has RI, UV, MALS, & viscometry 💪 We're really excited to start getting some absolute molecular weights!
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Reposted by Data-Driven Organic Materials Lab
Martin Seifrid @mseifrid.bsky.social · 28/05/2024
Our exploration of #ML to predict #OPV device performance from molecular structure of the materials *and* processing data is now officially published in J Mater Chem A as part of their 2024 Emerging Investigators series! doi.org/10.1039/D4TA...
doi.org
Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices
Our study explores the current state of machine learning (ML) as applied to predicting and designing organic photovoltaic (OPV) devices. We outline key considerations for selecting the method of encod...
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Data-Driven Organic Materials Lab @ddomlab.org · 18/12/2023
The semester's big achievement was installing our new Opentrons Flex. Only the 25th unit installed!
The 25th Opentrons Flex
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Data-Driven Organic Materials Lab @ddomlab.org · 18/12/2023
First semester at NC State MSE is in the books! Not only is NC State Engineering building the future of #SelfDrivingLabs, but there's an awesome self-driving library #MatSky #Chemsky
A robot retrieve books from the library stacks
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