Nathan Szymanski

Assistant professor @UCLA Materials Science and Engineering.

[Nathan Szymanski @NJSzymanski]

New paper out in @ACSPublications introducing some topological descriptors of electron density for inorganic materials! We hope these can supplement existing descriptors like Bader charges and serve as compact inputs for machine learning

https://t.co/1QvnaFoThg

[Bharat Jalan @JalanMbeLab]

Just published in @NanoLetters In close collaboration with Chris Bartel's group, We reveal growth mechanisms of rutile GeO₂ and (Sn,Ge)O2 films via hybrid MBE. #WideBandgapSemiconductors #HighPowerDevices @NSF @AFOSR @UMNCSE
https://t.co/xDY32Crh7a

[Nathan Szymanski @NJSzymanski]

Some new work from Shilong Wang in the @cedergroup on a method for efficient and acid-free extraction of Li from spodumene, a common mineral used in battery manufacturing - happy to be a part of this!

https://t.co/HHHiI8U8G9

[Nathan Szymanski @NJSzymanski]

@MBianchini87 @ScienceAdvances Thanks Matteo!

[Nathan Szymanski @NJSzymanski]

Our latest work on understanding solid-state synthesis is out now in @ScienceAdvances where we use in-situ XRD to show that initial reaction products can (*sometimes*) be predicted using DFT-computed thermochemical data! https://t.co/vdMrHO4E3D

[Nathan Szymanski @NJSzymanski]

And this proposed threshold was calibrated using 37 different reaction pathways targeting alkali metal oxides - more work is needed to confirm how this might vary in different chemistries!

[Nathan Szymanski @NJSzymanski]

The caveat here is that reliable predictions can only be made when the driving force to form one product is much larger (we propose a threshold of ~60 meV/atom) than any other competing driving forces

[Yuxing Fei @yuxing_fei]

🚨 Preprint Alert 🚨

How to manage experiment workflows in an autonomous lab just like managing computational workflows?

We proposed a general framework to manage devices and experiments in the lab, with human-in-the-loop feature built in.

https://t.co/Y9WWMNxDHu

[Nathan Szymanski @NJSzymanski]

New perspective out in @ACSEnergyLett where @ChrisJBartel and I share our thoughts on how computations have and will continue to play a role in guiding the synthesis of battery materials. Check it out! https://t.co/5zpyOd56VS

[Nathan Szymanski @NJSzymanski]

@julia_h_yang @GTChBE @GeorgiaTech Congrats, Julia! 🥳 Looking forward to seeing all the great work from your group

[Yan Zeng @YanHelenZENG]

Check out our latest work on Selective formation of metastable polymorphs in solid-state synthesis | Science Advances https://t.co/W4dGA3yUVr

We introduce a framework that uses both reaction energy and surface energy for effective polymorph selection.
@NJSzymanski @cedergroup

[Ceder Group @cedergroup]

Joining the A-Lab conversation: https://t.co/E5fnbQAPDI

[Nathan Szymanski @NJSzymanski]

Here's a video of the A-lab in action, courtesy of @BerkeleyLab

[Nathan Szymanski @NJSzymanski]

Excited to share our work in @Nature on the A-Lab, an AI-driven robotic platform that automates materials synthesis. It discovered 41 new compounds in 17 days of closed-loop experiments! Thanks to @cedergroup and @YanHelenZENG for leading this effort!

https://t.co/GLc8cHhoYh

[Nathan Szymanski @NJSzymanski]

@charlesxjyang @NatureComms Yep, these were all done manually by Pragnay, our very ambitious undergrad. More work on integrating this algorithm with automated experiments will be coming soon!

[Nathan Szymanski @NJSzymanski]

Our method to automate the optimization of solid-state synthesis is out now in @NatureComms! It uses DFT data but also learns from experiments to find the best precursors for a given material. We hope this can help guide future autonomous platforms 🤖 😃
https://t.co/T9CVT3Q8jt

[Nathan Szymanski @NJSzymanski]

Short-range order plays an important role in the performance of DRX cathodes 🔋 check out our latest work in @ChemMater where we show how pair distribution function data can be used to study this property!

Thanks to @Bin_Ouyang_CMS and @ChrisJBartel

https://t.co/JsSZdtd2cs

[Nathan Szymanski @NJSzymanski]

Happy to be a part of this work! Check it out if you're interested in using neural networks to analyze data from XRD, NMR, or Raman spectroscopy 🔬

[Jan Schuetzke @jschuetzke]

Proud to present our recent work on the evaluation of neural network architectures for classifying spectroscopic data. The article is available in @Nature_NPJ.
https://t.co/fzpvUH8iet

[Nathan Szymanski @NJSzymanski]

Excited to share our recent work in @Nature_NPJ, where we show that machine learning can be used to "drive" XRD measurements and identify phases in real time!

Thanks to @ChrisJBartel @YanHelenZENG @delmouha

https://t.co/eSb8ZgDC6l

[Nathan Szymanski @NJSzymanski]

For those interested in synthesis science and DRX cathodes, check out our latest article in @ChemMater showing several factors that govern fluorination.

Big thanks to all the great collaborators! @YanHelenZENG @cedergroup @ChrisJBartel @ClementGroupSB

https://t.co/qWqc1vgZfb