Philipp Benner

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Philipp Benner

Philipp Benner

@PhilippBenner2

{ML,Statistics}@{Materials,Bioinformatics} https://t.co/NbLUJTupg0 Now at: https://t.co/1UGkek36l5

Katılım Eylül 2020
460 Takip Edilen246 Takipçiler
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Philipp Benner
Philipp Benner@PhilippBenner2·
New high-performance Rust genomics libary specialized in handling BAM and BigWig files, and much more... docs.rs/crate/rustynet…
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Prof. Karl Lauterbach
Prof. Karl Lauterbach@Karl_Lauterbach·
Die Investitionen in die Wasserstoffwirtschaft brechen ein. Eine Katastrophe mit Ansage. Davon hängt ab, ob wir Industrieland bleiben können. Gas und Atom sind keine Lösung. Wir haben weder Atom- noch CO2 Endlager. share.google/KzBLUYI7Mzjf2h…
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Janosh
Janosh@jrib_·
PSA for machine learning force field authors who already have or are planning to submit models to Matbench Discovery. There's a new dynamic scatter plot on the landing page (…tbench-discovery.materialsproject.org) that allows comparing all 30 existing models against each other across 2 dozen metrics, hyper-parameters and model design choices. We hope that as new models and metrics get added to the leaderboard, this will surface empirical insights about which aspects of a model's design space positively or negatively impact certain metrics. For users of these ML force fields, we hope the dynamic nature of this plot will allow constructing exactly the Pareto front of metrics they care about to make a more informed decision on which potential best suits their research needs. To make this plot more informative, we encourage future model submission to be as detailed as possible in reporting the hyper-parameters and training procedure chosen for a given model. Existing submissions are welcome to backfill any data that wasn't originally recorded but could be helpful in this plot! There's an existing model schema on how to report e.g. graph construction cutoffs, max neighbor limits, learnings rates, layer counts, number of trainable parameters, etc. which will require extension to capture less common hyper-parameters as well as pre-training and fine-tuning stages in a standardized way. Happy to collaborate with everyone on that! The next step for this dynamic scatter plot is to report each model's inference time and memory usage at different system sizes. This will become the default values shown on the X axis as they establish a continuously updated cost-accuracy Pareto front for any metric on Matbench Discovery.
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Sandip De
Sandip De@SandipDeScience·
🚀 MLIPX is Live! 🌟 Check out our new open-source code from @BASF for evaluating machine-learned interatomic potentials. Dive into advanced evaluation methods, visualisation tools, and more! Special shout out to @PythonFZ and Sheena agarwal! github.com/basf/mlipx
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Yi-Lun Liao
Yi-Lun Liao@yilunliao·
EquiformerV2 [1] + DeNS [2] is now the best model on Matbench Discovery [3] (as of Oct. 18, 2024). Nicely done by Meta FAIR Chemistry team in their work [4]. Equivariance + Transformers + self-supervised learning indeed work pretty well on 3D atomistic data! [1] arxiv.org/abs/2306.12059 [2] arxiv.org/abs/2403.09549 [3] …tbench-discovery.materialsproject.org [4] arxiv.org/abs/2410.12771
FAIR Chemistry@OpenCatalyst

Our EquiformerV2 (86M) model pre-trained on OMat and fine-tuned on MPtraj and a subset of Alexandria, is state-of-the-art on Matbench discovery across all metrics with an F1 score > 0.9 and an accuracy of 20 meV/atom. 4/x

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Philipp Benner
Philipp Benner@PhilippBenner2·
Opportunity for a permanent machine learning researcher in the eScience group, who should develop their own research direction and profit from our nice and diverse team at @BAMResearch: If you love coding, stats, ML, and materials, please apply: bam.de/umantis/EN/201…
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Philipp Benner
Philipp Benner@PhilippBenner2·
Two more weeks to apply! 😎
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Philipp Benner
Philipp Benner@PhilippBenner2·
We're looking for a talented postdoc to join our eScience group! If you have strong skills in machine learning and excellent programming abilities, we want to hear from you. We are a diverse team and offer many challenging projects! bam.de/umantis/EN/197…
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Philipp Benner
Philipp Benner@PhilippBenner2·
@jrib_ @SamMBlau I see non-compliant models regardless of whether the switch is on or off. 🤔 Firefox 115.14.0esr
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Janosh
Janosh@jrib_·
@SamMBlau back from vacation. :) fixed in github.com/janosh/matbenc…. the new toggle calls them "non-compliant" models and a hover tooltip explains in what ways models can diverge from the Matbench Discovery requirements and why we nonetheless opt to show these models behind a toggle …tbench-discovery.materialsproject.org
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Sam Blau@SamMBlau

@MarkNeumannnn Great work! Question - why is ORB listed as a proprietary model? Seems like all of the data (from MPtraj + Alexandria) is open source, along with the model architecture? @jrib_ , am I missing something? Thanks!

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Stephan Poppe
Stephan Poppe@poppe_stephan·
Good News: An der philologischen Fakultät der @UniLeipzig wurde eine Position für eine:n Statistikbeauftragte:n geschaffen und ausgeschrieben (TVL13, 50%, unbefristet!). Hier die Details, falls das was für Euch ist: uni-leipzig.de/stellenausschr…
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Philipp Benner
Philipp Benner@PhilippBenner2·
🚀 Join BAM as a Researcher in Research Data Management! Integrate a central data platform and collaborate with top scientists. We offer a permanent contract, a competitive salary and a fun team! Shape the future of materials research digitalization! 🔗 bam.de/umantis/EN/193…
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Philipp Benner
Philipp Benner@PhilippBenner2·
We're looking for a talented postdoc to join our eScience group! If you have strong skills in machine learning and excellent programming abilities, we want to hear from you. Come help us push the boundaries of materials science! 🔗bam.de/umantis/EN/193…
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