Seifrid research group in MSE at NC State
Raleigh, NC
Joined November 2023
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Out now in @aplmachinelearn: the group's first-ever publication!! 🥳
doi.org/10.1063/5.0303721
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."
We think there are important implications for anyone using ML to guide #polymer design or build a #SelfDrivingLab.
Data-Driven Organic Materials Lab retweeted
Not to toot my own horn, but I think this is incredibly important and a very difficult challenge to overcome quickly. Hopefully our call to action can spur faster progress!
I’m excited to share a Call To Action I organized with @RenPhilanthropy "On the Need for Autonomous Science Instruments"
Signed by 25 leading researchers across the U.S., U.K., Canada, and Japan, we call for a new generation of autonomous science instruments based on three core pillars:
⚙️ Open Data & Software APIs
🤖 Design-for-Automation
🧩 Instrument Modularity
We also published a press release supporting the Call To Action, which includes endorsing quotes from AI & science leaders: @Kevinweil (OpenAI), @AndyHickl (Allen Institute), @jrkelly (Gingko), @teresasmeyer (Carnegie Mellon), @smc_ (Acceleration Consortium), and Michael Brenner (Harvard/Deepmind).
🚨🚨 The group's first preprint is up on
@ChemRxiv! 🥳🥳
Robust Learning from Literature Data: Model Generalizability and Uncertainty for Predicting Conjugated Polymer Solution Conformation
📝 doi.org/10.26434/chemrxiv-20…
Conventional or #selfdrivinglab experiments can be informed by data gathered from the literature.
Important scientific challenges often require the development of previously unknown materials: materials discovery.
#machinelearning models are not designed for this scenario...
Data-Driven Organic Materials Lab retweeted
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?
Data-Driven Organic Materials Lab retweeted
Congrats to #Scialog AUT awardees Mark Hendricks @whitmancollege, Jessica Sampson @jesstheligand @UDResearch @ChemistryUD & Martin Seifrid @M_Seifrid @ddom_lab @ncstatemse @NCStateEngr @NCState!
Another instrument is up and running! We set up our @GyrosProteinTec PurePep Chorus last Friday. Keep an eye out for exciting developments on this front 👀
We just completed the installation of Big Purchase #1™️: a quadruple (!) detector SEC from @TosohBio. This bad boy has RI, UV, MALS, & viscometry 💪 We're really excited to start getting some absolute molecular weights!
Data-Driven Organic Materials Lab retweeted
RCSA, @BeckmanFnd & Frederick Gardner Cottrell Foundation announce awards to 7 multidisciplinary teams in the 1st year of #Scialog: Automating Chemical Laboratories, which aims to accelerate innovation & broaden access within the chemical enterprise. bit.ly/4bMpWru
Data-Driven Organic Materials Lab retweeted
Congratulations to @M_Seifrid @ddom_lab @ncstatemse @NCStateEngr & @CoryMSimon @EngineeringOSU & @cgbischak @UtahChemistry @uofu_science for “Reducing the Cost of Device Development with Closed-Loop Proxy Measurements and Supplemental Characterization.”
Data-Driven Organic Materials Lab retweeted
Our exploration of machine learning to predict #OPV device performance from molecular structure of the materials *and* processing data is now officially published in @JMaterChem A as part of their 2024 Emerging Investigators series!
doi.org/10.1039/D4TA01942C
First semester at @ncstatemse is in the books! Not only is @NCStateEngr building the future of #SelfDrivingLabs, but there's an awesome self-driving library
The semester's big achievement was installing our new @opentrons Flex