prev: PhD @ Ghent University 🇧🇪, AI researcher @ Orbital Materials 🇬🇧
now: postdoc @ Rotskoff group (Stanford University)
We used a ridgeline (Joy Division inspired) chart to visualise daily temperature anomalies since 1940.
2024 clearly stands out with 100% of its days above 1.3C and 75% above 1.5C.
We used a ridgeline (Joy Division inspired) chart to visualise daily temperature anomalies since 1940.
2024 clearly stands out with 100% of its days above 1.3C and 75% above 1.5C.
typst.app
github.com/3b1b/manim
typst.app
github.com/3b1b/manim
We got this idea after their cool work on improving Plug and Play with FM: arxiv.org/abs/2410.02423
We got this idea after their cool work on improving Plug and Play with FM: arxiv.org/abs/2410.02423
scientific: ML potentials, DFT and post-HF calculations, (path-integral) MD, replica exchange, alchemical ΔF , hessians, ...
technical: automated job submission, simple Python, scales to >100 nodes, containerized!
scientific: ML potentials, DFT and post-HF calculations, (path-integral) MD, replica exchange, alchemical ΔF , hessians, ...
technical: automated job submission, simple Python, scales to >100 nodes, containerized!
gist? Train classifier in feature space of pretrained GNN to predict 'phase' of an atomic geometry:
CV(A->B) = logit(B) - logit(A)
+data-efficient
+invariant wrt trans/rot/perm
+compatible w foundation models!
gist? Train classifier in feature space of pretrained GNN to predict 'phase' of an atomic geometry:
CV(A->B) = logit(B) - logit(A)
+data-efficient
+invariant wrt trans/rot/perm
+compatible w foundation models!