Pending AI has been invited to participate in the @AWS and @peakxvpartners Generative AI United States Delegation in Palo Alto, California this week. Our Vice President, Business Development, David Almeida Cardoso, PhD, is in attendance.
🚨 Job alert! 🚨
Three (!) postdoctoral positions in quantum computing for chemistry
Total package: $119k – $148k p.a. for up to 4 years
Some details below; apply by 11 March here: https://t.co/YZodbcsdFy
After 1.5 yeas our review “Integrating #QSAR modelling and deep learning in #drugdiscovery: the emergence of deep QSAR” in @NatRevDrugDisc is out! Great team effort with Alex Tropsha, Alexandre Varnek, Gisbert Schneider, and Art Cherkasov #compchem https://t.co/qloy60Fgzd
Delighted to share our recent work in which we developed MeGen, an RL model, to efficiently generate low-energy Ga cluster 3D structures. MeGen outperforms the conventional approach in time and resources for generating ground-state (GS) geometries and low-lying isomers.
Hey, chemtweeps, @gabepgomes & I have a joint postdoc #chempostdoc opening. The position will focus on developing ML/AI algorithms for autonomous experimentation at @CarnegieMellon Cloud Lab https://t.co/uySD3IRp6A. Opps to work with @NSF_CCAS! #compchem
https://t.co/RUJM4aWifQ
The ELLIS ML4Molecules workshop will also happen this year on November 28 in VIRTUAL format!
Please find the announcement and the call for papers here: https://t.co/13yua2tPKr
Looking forward to your contributions!
A new paper just published in @JCIM_JCTC! Auto3D: Automatic Generation of the Low-Energy 3D Structures with ANI Neural Network Potentials. Collaboration with @adrian_roitberg#compchem#MachineLearning https://t.co/hYTRpcyl2j
I can't believe that our book “Quantum Chemistry in the Age of Machine Learning” is finally out!
I can't express enough my gratitude to everyone involved!
https://t.co/ENOI7TVEwz
I am beyond excited to announce that QMugs, our large open-source database of drug-like molecules alongside their computed quantum mechanical properties, is now available on @ScientificData! 🧵[1/5]
(work w. @clemensisert & @atzkenneth)
https://t.co/l0XPJZAT4M
We develop a new #MachineLearning scheme for protein pKa. It is based on representation learning & combines atomic environment vector (AEV) and learned QM representation from ANI-2x neural network potential. Our latest paper in @ChemicalScience https://t.co/AK7bytZdiF #compchem
A machine-learning model as Mat2Spec based on probabilistic embedding enables to predict materials’ phonon density of states and electronic density of states instead of single scalar properties https://t.co/NwV27uYyxE #mlchem#compchem @johnmgregoire@Caltech@BerkeleyLab@Cornell