As a PhD student myself and interacting with a lot others, my genuine takeaway is that most of us in cogsci don't have strong shared basics! As a TA I often refer to this playlist by @langofmind who did a great job at explaining cogsci with rigor & clarity https://t.co/Z5zQAac8CW
We’re pleased to welcome 𝗖𝗮𝗺𝗯𝗿𝗶𝗱𝗴𝗲 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗣𝗿𝗲𝘀𝘀 as an exhibitor at NetSci 2026 📚 Since 1534, Cambridge has published influential works spanning graph theory, network analysis, dynamics, and spreading processes. 🔗 https://t.co/Edh3paWNNZ
When does a chemistry ML model need to know the electrons, and when is geometry enough?
Machine-learning models for molecular properties come in two flavors. Some take only nuclear charges and 3D coordinates, through descriptors like SLATM, FCHL, SOAP, or geometric deep learning models such as MACE. Others (the SPAHM family, OrbNet, MOB-ML, OrbitAll) explicitly inject electronic information from a guess Hamiltonian or localized orbital analysis. For neutral closed-shell organic molecules both classes typically work well. For transition metal complexes, where the same nuclear arrangement can carry very different charge and spin states, the question is sharper: when does electronic information actually pay off?
Yuri Cho and coauthors run that benchmark cleanly. They evaluate KRR with the descriptor zoo above, plus MACE and 3DMol (a molecular variant of their 3DReact model) with optional charge and spin embeddings, on three TM complex datasets: TM-GSspin+ (3d metals, charge -5 to +4, ground-state spin), tmPHOTO (3d-4d-5d, mostly neutral, singlet-only), and Octa-MK (octahedral 3d, paired LS/HS geometries). Targets are spin-splitting energies, HOMO, LUMO, the gap, and dipole magnitudes.
The result splits cleanly. For spin-splitting energies and frontier orbital energies, whose distributions shift strongly with charge and spin, electronically informed models win. MACE-QS reaches 0.21-0.35 eV MAE for HOMO across the three datasets, and 3-SPAHM is the best KRR descriptor for HOMO.
For the HOMO-LUMO gap and dipole magnitude, whose distributions are roughly insensitive to charge state, structure-only models pull ahead. SLATM benefits from strong cancellation between correlated HOMO and LUMO errors (R^2 ~0.82 in TM-GSspin+), and AtomicDipolesMACE wins on dipoles by a wide margin by predicting the full vector then taking its magnitude.
A practical takeaway: 3DMol with charge and spin embeddings hits accuracy close to MACE-QS but trains in ~40 seconds versus 4000-15000 for MACE on the same subset, useful when iteration speed beats the last few meV.
For homogeneous catalysis, photoredox, or magnetic materials: check your property's dependence on charge and spin before picking the model. If those electronic states drive the distribution, pay for an electronically informed architecture; if not, a fast structure-only model will likely match or beat it.
Paper: Cho et al., Digital Discovery (2026) — CC BY 3.0 | https://t.co/Md9LteLPrv
Video lectures, SUSTech MEE 5114 Advanced Control for Robotics ( Screw Theory, Nonlinear Control and Optimal Control ) spring 2022, by Wei Zhang
https://t.co/wyuqsFap60
https://t.co/SgKRmfsGJh
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I gave a talk at ICLR 2026 about how we are scaling RL on frontier LLMs with 1T+ parameters, on experimental data from our physical lab at Periodic!
Here's a rough recording of the talk:
From "Mathematical theory of deep learning: Can we do it? Should we do it?" to "There Will Be a Scientific Theory of Deep Learning".
It's respectively the title of a talk I gave four years ago, and the title of an arxiv paper from four days ago.
I really like the "learning mechanics" perspective (think of it as a continuation of "statistical mechanics", "quantum mechanics", and so on). Several of my last academic papers can be viewed under that lens (e.g. Learning threshold neurons via the “edge of stability”; or LEGO). I'm not as optimistic as the authors of the recent arxiv paper that we will EVER be able to reach what the "physics mechanics" field have achieved, but it's certainly worth trying.
Talk: https://t.co/QsBPHP0fDm
Paper: https://t.co/N4JSj3AkYZ