Florian Häse

[https://bsky.app/profile/aidd.bsky.social @AiddOne]

Florian introduces SELFIES

[Tobias Morawietz @TobiasMorawietz]

We are thrilled to announce the !AI.QU. project on bridging AI & QUantum mechanics 🤖🖥️ for rapid, accurate, and scalable in silico predictions, welcoming our first partners Nong Artrith @UniUtrecht @UUBeta & @faccts_orca
!AI.QU. is funded by @Bayer's Life Science Collaboration

[Florian Häse @florian_hase]

Very excited about this immense interest in #selfies - can't wait to see what the #compchem (and other?) communities will accomplish with #selfies in 2022!

[Machine Learning: Science and Technology @MLSTjournal]

NOW >15,000 DOWNLOADS!🥳'Self-referencing embedded strings (#SELFIES)..'by @A_Aspuru_Guzik @MarioKrenn6240 @florian_hase @P_Friederich @akshat_ai @UofT @chemuoft @VectorInst @CIFAR_News @acceleration_c - https://t.co/GF9edPJVDz #compchem #materials #machinelearning #SMILES #AI

[Mario Krenn @MarioKrenn6240]

pip install selfies

Some fun🤩
- - -
import selfies
a=selfies.encoder('CC(NC)CC1=CC=C(OCO2)C2=C1')
b=selfies.decoder(a)
- - -

More details:
https://t.co/JUGOLjpmSd

@akshat_ai @florian_hase @P_Friederich @A_Aspuru_Guzik #matterlab

[Machine Learning: Science and Technology @MLSTjournal]

NOW >12,000 DOWNLOADS! 'Self-referencing embedded strings (#SELFIES)...'by @A_Aspuru_Guzik @MarioKrenn6240 @florian_hase @P_Friederich and @akshat_ai @UofT @chemuoft @VectorInst @CIFAR_News @acceleration_c - https://t.co/GF9edPJVDz #compchem #materials #machinelearning #opendata

[Acceleration Consortium (AC) @acceleration_c]

We're a day late, but in honour of #AdaLovelaceDay21 meet ADA, a #selfdriving lab for thin-film materials discovery and optimization in the #cleanenergy sector + beyond. ADA is a collab between the AC's @A_Aspuru_Guzik @cpbCleanEnergy @procrastiprof. ▶️ https://t.co/IkF5PpNXSb

[Mario Krenn @MarioKrenn6240]

SELFIES (100% robust molecular string representation for #MachineLearning in Chemistry) just got its 300th Github-💫! 🥳

[GH] https://t.co/VX04qGjrcv
[Paper] https://t.co/lHOPsfZfwU

- massive multi-author perspective in the making (send mail if curious)
- version 2.0 very soon!

[Machine Learning: Science and Technology @MLSTjournal]

Still our most read article -'Self-referencing embedded strings (#SELFIES)..' by @A_Aspuru_Guzik @MarioKrenn6240 @florian_hase @P_Friederich and @akshat_ai @UofT @chemuoft @VectorInst @CIFAR_News @acceleration_c hits 11,000 downloads! https://t.co/GF9edPJVDz #compchem #graphs #AI

[Machine Learning: Science and Technology @MLSTjournal]

'Olympus: a benchmarking framework for noisy #optimization.....' by @florian_hase @matteo_aldeghi @A_Aspuru_Guzik @ElenaScully @procrastiprof @loic_roch @chemchristensen @UofT @chemuoft @CIFAR_News hits 1000 downloads!- https://t.co/vnmdtrkIit #compchem #machinelearning #opendata

[IOP Publishing @IOPPublishing]

Don't forget to register for the #SELFIES workshop: Exploring the challenges + opportunities for string-based representations in chemistry with @acceleration_c & @MLSTjournal. Featuring @A_Aspuru_Guzik, @akshat_ai & @MarioKrenn6240. Register here:
https://t.co/5Xlx5N94LN #AI

[Machine Learning: Science and Technology @MLSTjournal]

Join @MLSTjournal and @acceleration_c Aug 13 for our joint #SELFIES workshop, exploring the challenges + opportunities for string-based representations in chemistry. LIMITED SPOTS; register now!
https://t.co/HxncVY8Bxz
#compchem #machinelearning #AI

[Mario Krenn @MarioKrenn6240]

Great milestone: #SELFIES (our 100% robust molecular string representation) downloaded more than 9000 times! 🤘😃

Paper: https://t.co/lHOPsggQVu
GitHub: https://t.co/VX04qGB2B5

News very soon!

@MLSTjournal @florian_hase @akshat_ai @P_Friederich @A_Aspuru_Guzik @acceleration_c

[Machine Learning: Science and Technology @MLSTjournal]

'Olympus: a benchmarking framework for noisy #optimization......' by @florian_hase @matteo_aldeghi @A_Aspuru_Guzik @ElenaScully @procrastiprof @loic_roch @chemchristensen @UofT @chemuoft @CIFAR_News hits 500 downloads!- https://t.co/vnmdtrkIit #compchem #machinelearning #Python

[Tejs Vegge @TVegge]

Meet @A_Aspuru_Guzik and his favourite pets, Phoenics, Chimera and Gryffin! Exceptional talk by Alán about #MADNESS and the perspectives of autonomous materials discovery at the CAMD webinar series #Discovery #AI #materials #innovation @MICleanEnergyRD @DTU_CatTheory @DTUtweet

[Mario Krenn @MarioKrenn6240]

Want to learn about SELFIES, a 100% valid molecular graph representation for scientific discovery & deep generative models? Then join my talk on Thursday, 12.00 (EST) with the AI Socratic Circles @AISC_TO
: https://t.co/CU0lrmNyN9 #DeepLearning #compchem https://t.co/mhEawCiHb3

[Akshat Nigam @akshat_ai]

First Workshop paper: #SELFIES in the NeurIPS Graph Representation Learning workshop! Check out the full paper (https://t.co/WNfYEGpE3E). Work done with @MarioKrenn6240 @florian_hase @P_Friederich in #matterlab 😀

[Mario Krenn @MarioKrenn6240]

We updated #SELFIES representation for molecules: https://t.co/VX04qGB2B5 - now its validity is >99.99% (even for entirely random sequences). Perfect for #MachineLearning in #chemistry, in particular for generative models. (details: https://t.co/mhEawCiHb3)

[Mario Krenn @MarioKrenn6240]

Are you interested in how to use #SELFIES (https://t.co/mhEawCiHb3) in a generative model for molecular design? See our new github addition, SELFIES in variational autoencoders (VAE): https://t.co/RadkBDjZ9e feedback welcome! @florian_hase @akshat_ai @P_Friederich @A_Aspuru_Guzik

[Florian Häse @florian_hase]

Excited to find our perspective on self-driving laboratories on this list! #chemos #matterlab

[Trends in Chemistry @TrendsChemistry]

*Free access* to our four "most read" articles over the month of June. Wonderful contributions from: @A_Aspuru_Guzik (https://t.co/JCQIjOoVo7); @johnsonchem (https://t.co/VjXVfStg04); @UT_YuGroup (https://t.co/ls4nYAv5jP); and @Farhomies (https://t.co/FtQQ50YSnU). #RealTimeChem