SPP 2363 Molecular Machine Learning | Developing ML solutions for molecular problems | Interdisciplinary and collaborative Priority Program funded by the DFG
Congratulations to Felix Katzenburg (@FKatzenburg )🐈⬛🏰 on successfully defending his PhD! 🎓 🥂 🏆 We're incredibly happy to have been part of this journey, and wish you all the best for your next chapter at Alán Aspuru-Guzik's group!
#PhDone
Thrilled to share our hybrid inverse design strategy for discovering organic photocatalysts, now published in @JACS: Accelerating EnT catalyst discovery by bridging generative modeling and experimental validation.
pubs.acs.org/doi/10.1021/jac…
8th Molecular Machine Learning Symposium - ONLINE!
15th Jan 2026, 3pm UTC+1 - Safe the date!
More information to follow shortly...
Registration link:
uni-ms.zoom-x.de/webinar/reg…
Richard Willstätter Lecture by Ilan Marek, Technion
Excellent talk about non-classical carbocations, followed by presentation from PhD student Noam Orbach on housanes. 👏😀👏This concludes @GDCh_aktuell lecture tour (Freiburg, Munich and Muenster).
en.gdch.de/gdch/prizes-and-a…
Liebig-Lecturer 2025: Prof. Renana Gershoni-Poranne
Award by @GDCh_aktuell. Lecture tour ongoing, talk @uni_muenster was a blast:
Mission ImPASsible: Decoding Polycyclic Aromatic Systems with Deep Learning 👍 🖥️ 😊
@PoranneG with team MolMachLearning of @GloriusGroup@spp2363
Creativity is the engine of discovery ✨ Fantastic PhD workshop with @ilona_lipp on strategies to creatively tackle research problems and explore new avenues for molecular machine learning🔬🚀
Today marked a great start of the second funding period of the SPP 2363 with the first day of our Kick-off meeting at the @Leopoldina in Halle.
Special thanks to Esther Heid for an truly inspiring talk connecting molecular representation, model architectures and reactivity! 🧪💻
Check out Prof. Schreiners interview in the upcoming edition of the "KI-Buzzer" magazine. He shares his view on AI and education🎓
kibuzzer.com/de/mag/v5/kibuz…@prsgroupjlu
The next chapter for SPP 2363 has begun!🌟 The second funding phase is officially underway. A warm welcome to all the new PIs and students! We're excited to build upon the momentum of our first phase and and driving new discoveries together🚀
#spp2363#MML#Collaboration#Network
New Publication 👏
We are excited to share our latest paper, "Apax: A Flexible and Performant Framework for the Development of Machine-Learned Interatomic Potentials", published in the JCIM.
Read the full paper: pubs.acs.org/doi/10.1021/acs…
Code: github.com/apax-hub/apax
The @GloriusGroup is calling for a passionate postdoc who wants to shape the future of digital chemistry – where ML models, mass spectrometry, and human insight meet. ⚗️💻😀
In collaboration with: lab25, ChemInnovation 🚀
🙏 RT #chemistry
Jobposting:
stellen.uni-muenster.de/jobp…
How can data science & #MachineLearning transform reaction optimization? 🤖🧪
Matt Sigman (@Sigman_Lab) wraps up the #7thMML#Symposium with a deep dive into tools for analyzing structure-function relationships, optimizing reactions, and designing smarter experiments! 🧠✨
Need to predict material properties? 🔮🧪
@ChengBingqing is now presenting at the #7thMML#Symposium! Learn how she combines statistical mechanics & #MachineLearning interatomic potentials to compute phase diagrams, chemical potentials, and reactions on surfaces! 🤖✨
Solvent effects? Reaction dynamics? 🔄🌊
Fernanda Duarte (@fjduarteg) is now on stage at the #7thMML Symposium discussing how #MachineLearning Potentials (MLPs) balance efficiency & accuracy in modeling chemical reactions in solution! 🧪🤖
How can #MachineLearning accelerate the discovery of transition metal-containing materials?
🎤Heather J. Kulik (@KulikGroup) is kicking off the #7thMML Symposium!
🧪 “Getting from the Computer to Real World Materials Faster with Machine Learning”