@NJSzymanski

Assistant professor @UCLA Materials Science and Engineering.

szymanski-research-group.com/
Joined September 2013
  • Tweets25
  • Following274
  • Followers466
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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 doi.org/10.1021/acsmaterials…

Topological Descriptors for the Electron Density of Inorganic Solids

Abstract. Descriptors play an important role in data-driven materials design. While most descriptors of crystalline materials emphasize structure and compo

pubs.acs.org
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Bharat Jalan@JalanMbeLab27 Dec 2024
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 pubs.acs.org/doi/epdf/10.102…

Unraveling the Growth Dynamics of Rutile Sn1–xGexO2 Using Theory and Experiment

Abstract. Rutile GeO2 and related materials are attracting interest due to their ultrawide band gaps and potential for ambipolar doping in high-power elect

pubs.acs.org
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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! pubs.acs.org/doi/10.1021/acs…

Direct Lithium Extraction from α‑Spodumene through Solid-State Reactions for Sustainable Li2CO3...

Abstract. With increasing battery demand comes a need for diversified Li sources beyond brines. Among all Li-bearing minerals, spodumene is most often used

pubs.acs.org
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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! doi.org/10.1126/sciadv.adp33…

Quantifying the regime of thermodynamic control for solid-state reactions during ternary metal...

In situ characterization reveals the conditions where reaction outcomes can be predicted using thermodynamic calculations.

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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
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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!
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Yuxing Fei@yuxing_fei24 May 2024
🚨 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. arxiv.org/abs/2405.13930
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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! doi.org/10.1021/acsenergylet…

Computationally Guided Synthesis of Battery Materials

Abstract. Materials synthesis is a critical step in the development of energy storage technologies, from the first synthesis of newly predicted materials t

pubs.acs.org
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Yan Zeng@YanHelenZENG18 Jan 2024
Check out our latest work on Selective formation of metastable polymorphs in solid-state synthesis | Science Advances science.org/doi/10.1126/scia… We introduce a framework that uses both reaction energy and surface energy for effective polymorph selection. @NJSzymanski @cedergroup

Selective formation of metastable polymorphs in solid-state synthesis

Metastable polymorphs with low surface energy are made accessible by reactions with large thermodynamic driving force.

science.org
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Ceder Group@cedergroup2 Dec 2023
Joining the A-Lab conversation: linkedin.com/feed/update/urn…

Regarding our recent A-Lab article | Gerbrand Ceder | 16 comments

Recently, our team published an article describing the A-Lab, an autonomous laboratory for AI-driven synthesis of targeted compounds. The objective of the paper is to demonstrate that autonomous...

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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! nature.com/articles/s41586-0…
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Here's a video of the A-lab in action, courtesy of @BerkeleyLab
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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 🤖 😃 nature.com/articles/s41467-0…
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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 pubs.acs.org/doi/10.1021/acs…

Modeling Short-Range Order in Disordered Rocksalt Cathodes by Pair Distribution Function Analysis

Abstract. Pair distribution function (PDF) analysis is a powerful technique for the characterization of short-range order (SRO) in disordered materials. Ac

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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@jschuetzke6 Jun 2023
Proud to present our recent work on the evaluation of neural network architectures for classifying spectroscopic data. The article is available in @Nature_NPJ. nature.com/articles/s41524-0…
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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 nature.com/articles/s41524-0…

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

npj Computational Materials - Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

nature.com
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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 pubs.acs.org/doi/10.1021/acs…

Understanding the Fluorination of Disordered Rocksalt Cathodes through Rational Exploration of...

Abstract. We have designed and tested several synthesis routes targeting a highly fluorinated disordered rocksalt (DRX) cathode, Li1.2Mn0.4Ti0.4O1.6F0.4, w

pubs.acs.org
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Nathan Szymanski retweeted
Jan Schuetzke@jschuetzke21 Jun 2022
We present a synthetic dataset generator to benchmark machine learning models on spectroscopic data. Spoiler: No neural network model we tested achieved a perfect accuracy. Manuscript: arxiv.org/abs/2206.06031 Code: github.com/jschuetzke/synthe…
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Happy to share our recent work in @ChemMater on automating phase identification for XRD spectra with a probabilistic CNN! Thanks to @cedergroup @ChrisJBartel Code: github.com/njszym/XRD-AutoAn… Paper: pubs.acs.org/doi/abs/10.1021…
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Check out our review on autonomous experimentation for novel inorganic materials synthesis, now published in @MaterHoriz! @cedergroup @ChrisJBartel @BerkeleyLab pubs.rsc.org/en/Content/Arti…

Toward autonomous design and synthesis of novel inorganic materials

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and devel

pubs.rsc.org
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