I was actually considering eos as well because of the recent manjaro drama ... what were your thoughts on why not plain arch instead of eos?

Ever wanted to run MD simulations of entire proteins in water with DFT accuracy?

Meet AMPv3-BMS25, the latest iteration of our AMP multiscale neural network potential by
@rinikerlab.bsky.social

Read more in the preprints:
doi.org/10.26434/che...
doi.org/10.26434/che...

I'll be at Pacifichem this week, hit me up if you wanna have a coffee! ☕

Join us today from 4:30 to 7:30 PM @neuripsconf.bsky.social Hall C,D,E #1006 for our poster on SmoothDiff, a novel XAI method leveraging automatic differentiation.
🧵1/6

I'm in San Diego this week for Neurips, hit me up if you wanna have a coffee ☕

physics is a branch of chemistry

I'm in Oslo this week for WATOC, hit me up if you wanna have a coffee ☕

GitHub - khaledkah/tv-snr-diffusion Contribute to khaledkah/tv-snr-diffusion development by creating an account on GitHub.

9/ Check it yourself:

🔗: github.com/khaledkah/tv...
📄: www.arxiv.org/abs/2502.08598

Thanks to Khaled, Winnie, Oliver, Klaus, and Shin for the cool collaboration as well as @bifold.berlin, TU Berlin, RIKEN, and DeepMind

8/ Takeaway: Exploding TV isn’t needed. Control TV + SNR separately for faster, better sampling. Method generalizes across domains (molecules, images).

7/ Why it works? Our empirical analysis shows:

1. Straight trajectories near data (t ≈ 0) are important (see in the inset plot)
2. Broad support of pₜ(𝐱) early on → robust to errors (note how SMLD goes from small to huge range instead of staying the same)

6/ Images: Matches EDM with uniform grid

No fancy time grids like in EDM needed! VP-ISSNR on CIFAR-10/FFHQ ≈ EDM but with fewer hyperparameters!

5/ Molecules in 8 Steps:

VP-ISSNR achieves 74% stability with 8 steps, 95% with 64 (SDE). Beats all baselines!

4/ We propose a new VP schedule 📈:

Exponential inverse sigmoid SNR (ISSNR)→ rapid decay at start/end. Generalizes Optimal Transport Flow Matching.

3/ VP variants improve existing schedules:

Take SMLD/EDM (exploding TV) → force TV=1. Result: +30% stability for molecules with 8 steps

(x-axis is NFE=number of function evals).

2/ Most schedules (like EDM by Karras or SMLD (Song & Ermon) let TV explode (VE=variance exploding).

We show constant TV (variance preserving, VP) + optimized SNR works better (ISSNR)!

(it's a wild table, sorry, but notice our VP variants I circled)

1/ Problem: Diffusion models are slow due to repeated evals but reducing steps hurts quality if the noise schedule isn’t optimal. Other schedules passively adjust variance. Can we do better?

🔑Insight: control Total Variance (TV) and signal-to-noise-ratio (SNR) independently!

We have a new paper on diffusion!📄

Faster diffusion models with total variance/signal-to-noise ratio disentanglement! ⚡️

Our new work shows how to generate stable molecules in sometimes as little 8 steps and match EDM’s image quality with a uniform time grid. 🧵

We are already on Day 3 of the workshop "Density Functional Theory and Artificial Intelligence learning from each other" in sunny CECAM-HQ.
This afternoon, Fang Liu (Emory University) & Michael Herbst (EPFL) will present their talks.

our GPU cluster tonight after the ICML deadline

Figure comparing automatic differentiation (AD) and automatic sparse differentiation (ASD). (a) Given a function f, AD backends return a function computing vector-Jacobian products (VJPs). (b) Standard AD computes Jacobians row-by-row by evaluating VJPs with all standard basis vectors. (c) ASD reduces the number of VJP evaluations by first detecting a sparsity pattern of non-zero values, coloring orthogonal rows in the pattern and simultaneously evaluating VJPs of orthogonal rows. The concepts shown in this figure directly translate to forward-mode, which computes Jacobians column-by-column instead of row-by-row.

You think Jacobian and Hessian matrices are prohibitively expensive to compute on your problem? Our latest preprint with @gdalle.bsky.social might change your mind!
arxiv.org/abs/2501.17737
🧵1/8

We submitted our stuff to the ICML today at lunch and I am so happy about it. It's juicy, it might get rejected, but it has the heart in the right place :]

acab includes the raclette police 💡

i guess their claim would be that it blows up for mysterious NN reasons rather than integrator or time step. 2 fs is a bit of chonky step, i agree, but if it explodes at say 0.1 fs i'd start wondering about the NN more than about the time step

stuff like this, (Ala)_2 at 2 fs or water at 1 fs? at <=0.5 fs they wouldn't explode for curl free forces, i assume?

why was that paper bad? i thought it was more of a benchmark than proposing their own thing anyways?

i raise you to ful midammis oml

Bluesky is emerging as the new platform for science - Mike Young Academy Scientific Twitter is about to find its true successor. And it is not X. This, our latest release, shows that the Bluesky network of scientists is growing — and growing.

A mapping of how Bluesky is becoming the new Scientific Twitter

mikeyoungacademy.dk/bluesky-is-e...

Seems like it refers to DFT. They actually give an intuition from metallurgy in the appendix about simulated annealing. I didn't know it's about removing impurities 🙉

Excellent question. Alas, I can't say i like mcmc (or tmcmc?) as a word either. A string of non-descript names is just ... Eugh

Ah yes, 'annealing', i do it every day and have a super intuitive understanding of what it is. In fact, im annealing right now.