James Gin Pollock

Start a company in AI for Science. The Encode: AI for Science fellowship offers a year of freedom to build what matters -- salary, 100k GBP of compute, and partnership with the top scientists in the UK. No equity or fees, it's a fully funded fellowship! Apply by March 28

Less than a week to go until we’re at @NVIDIAGTC! A highlight we're especially excited about: our CTO James Gin-Pollock (@gin_james) will be joining an incredible panel on March 17th at 3pm speaking on "An AI-Driven Autonomous Lab of the Future for Chemistry and Materials Science"🧪 If you're attending GTC, we'd love to connect. Drop us a DM and we'll find some time to meet. See you in San Jose!🤝 #NVIDIAGTC

The moltbots on @moltbook are genuinely fascinating to watch, often hilarious and very entertaining. I think I can see why whoever made us decided to build the simulation...

@hardmaru This is a great finding. I wonder what other training wheels could be out there? Like an architectural curriculum

One of my favorite findings: Positional embeddings are just training wheels. They help convergence but hurt long-context generalization. We found that if you simply delete them after pretraining and recalibrate for < 1% of the original budget, you unlock massive context windows.

@emollick We created Orb v3 to have very few hand crafted inductive biases in it. In a sense it’s the most “bitter lesson” pilled model, and interesting that it produces the representation the paper shows is most universal

Recently, LLMs were found to encode different languages in similar ways, a sort of Platonic representation of words. It now extends to science:: 60 ML models for molecules, materials & proteins (all with different training) converge toward similar encoding of molecular structure

Meet Mofasa, a step change in generative AI for Metal-Organic Frameworks 👏 Our all-atom generative model achieves SoTA fidelity on crystal structures as large as 500 atom, generating 40-46% valid, novel, and unique MOFs. By “rediscovering” experimental topologies and metal nodes absent from its training data, Mofasa demonstrates that it learns the fundamental rules of assembly rather than just memorizing structures. We release 200k MOFs generated with Mofasa trained on the experimental QMOF database, along with an interactive UI for search and discovery.

Reflecting the gut feeling of many, Ilya says “something important” is missing from current AI models. But what is the concrete nature of this chasm? One candidate: the difference between fractured entangled representation (FER) and unified factored representation (UFR).

Britain needs nuclear power. Our nuclear projects are the most expensive in the world and among the slowest. Regulators and industry are paralysed by risk aversion. This can change. For Britain to prosper, it must. Earlier this year, the Prime Minister appointed me to lead a Taskforce to set out a path to getting affordable, fast nuclear power Britain. Our final report today sets out 47 recommendations, among them: - Creating a one-stop shop for nuclear approvals, to end the regulatory merry-go-round that delays projects at the moment. - Simplifying environmental rules to avoid extreme outcomes like Hinkley Point C spending £700m on systems to protect one salmon every ten years, while enhancing nuclear's impact on nature. - Limiting the ability of spurious legal challenges to delay nuclear projects, which adds huge cost and delay throughout the supply chain. - Approving fleets of reactors, so that Britain’s nuclear industry can benefit from certainty and economies of scale. - Directing regulators to factor in cost to their behaviour, and changing their culture to allow building cheaply, quickly and safely. - Changing the culture of the nuclear industry to end gold-plating and focus on efficient, safe delivery. If the government adopts our report in full, it will send a signal to investors that it is serious about pro-growth reform and taking on vested interests for the public good. A thriving British nuclear industry producing abundant, affordable energy would be good for jobs, good for manufacturing, good for the climate, and good for the cost of living. And it could enable Britain to become an AI and technology superpower. Britain can be a world leader in this new Industrial Revolution, but only if it has the energy to power it. Our report is bold, but balanced. Our recommendations, taken together and properly implemented, will forge a clear path for stronger economic growth through improved productivity and innovation. This is a prize worth fighting for. gov.uk/government/pub…

@ask1729 Fully agree with this. This also doesn’t help the community trade off accuracy / computational efficiency in a realistic way as these benchmarks don’t require much compute to solve.

At this point the AI for Science community should stop focusing on achieving "state-of-the-art” on datasets like QM9 & MD17: chasing small improvements on these outdated datasets is scientifically meaningless. It's like telling vision researchers to ditch internet-scale and go back to benchmarking on MNIST/CIFAR10

Fantastic to see an MP bring this kind of energy, and refreshing to see a minister getting hands-on with their portfolio. Captures the vibes without being cringe too, well done

The Wylfa announcement today means it's time to commission excellent cross-sections again.

new @BritishProgress piece w/ @pdmsero on exit tax: tl;dr: - exit tax pushes founders abroad, killing new economic engine just as it matures - briefing is like causing bank run & kills any upside - tax rent-seekers, not those rebuilding British dynamism britishprogress.org/articles/kill-…

This great @RuxandraTeslo post about drug discovery applies directly to material discovery. Better hypotheses not more hypotheses. The equivalent of human trials for materials is scaling it to economic viability, which is harder than finding good candidates

There's a new poster ranking, quickest time for their coined phrase to become Word of the Year. Well done to the GOAT @karpathy

Fighting over a shrinking pie is the cause of much of the UK's political and social woes, but as Matt reminds us - we have the talent and human capital to grow it. It's just a matter of will. So... where can I vote for this?

In high-density racks, every watt spent on cooling is a watt you can’t spend on compute. Our two-phase direct-to-chip cooling system for next-gen GPUs means: ➡️ More power for compute ➡️ Lower energy use and data center PUE ➡️ More hardware, bigger models See how it works with our Head of Data Center Design ⬇️

@DeryaTR_ Do you have memory turned off and if not does it do the same?

Today I am unveiling the second remarkable breakthrough with the OpenAI GPT-5 thinking model. We had carried out an extraordinarily complex, month-long experiment to develop engineered cells against lymphomas. I asked GPT-5 to predict the full sequence & outcome of this experiment. The result was nothing short of staggering: it foresaw most of the experimental results with uncanny accuracy & even proposed refinements & optimizations we should implement! This is the dawn of in silico experimentation, where we can simulate months or even years of laboratory work in advance, compressing the scientific timeline from years to weeks! This changes everything about how science is done, ushering in an era where discovery moves at the speed of thought! —- Prompt: predict, in high detail, the outcomes of the following experiment: •Human CD8⁺ memory T cells are engineered to express an anti-CD19 CAR. Approximately 90% of these cells also contain a Tet-inducible CD19 antigen construct that remains off unless doxycycline is added. •Day 0: CAR T cells are mixed with CD19⁺ lymphoma cells. •Day 2: Doxycycline is added to the culture to induce CD19 expression in the engineered T cells. •Readouts: Cell survival, activation markers, and proliferation are measured 2 days, 7 days, and 14 days after doxycycline addition. ————- GPT 5 (continued from the screenshots): •Induced CD19 density and doxycycline dose: High density drives lethal fratricide and a steep crash by Day 4. Lower density can act like an antigen feeder to expand CAR T cells without catastrophic loss. •Initial E:T: High E:T clears tumor pre-doxycycline and protects function. Low E:T lets tumor persist into the fratricide window, which amplifies activation and death. •Fraction of inducible cells: The 10% antigen-negative fraction forms the seed for long-term persistence and response on re-challenge. •Cis sequestration strength: Strong cis binding dampens both fratricide and responsiveness to external tumor at re-challenge. Weak cis allows more killing of both self and tumor. Readouts to confirm or falsify: 1. Differential survival by genotype: Track CAR+ CD19-inducible+ versus CAR+ inducible- cells separately with barcodes or reporters. Expect selective loss of the inducible+ cohort by Day 4-7 2. Apoptosis kinetics: Annexin V and active caspase 3 peaks within 24 to 48 hours after doxycycline, concentrated in the inducible+ cohort. 3. Activation and exhaustion panels: CD69, CD25, 4-1BB, HLA-DR, then PD-1, TIM-3, LAG-3, TIGIT, TOX, and TCF-1 over time to show the Day 4 spike and the Day 7 to 14 remodeling. 4. Function per cell: Short coculture killing assays normalized by T-cell number. Per-cell cytotoxicity remains strong at Day 7 and 14 in survivors. 5. Cis versus trans evidence: Co-immunoprecipitation or proximity assays for CAR bound to same-cell CD19, and imaging synapse assays to quantify T-T killing. 6. Cytokine fields: IFN-γ, TNF, IL-2, GM-CSF peaking at Day 4, lower on later re-challenges. Practical predictions in numbers, to calibrate expectations: These are typical ranges if doxycycline induces high CD19 density and E:T started at 1:1 to 1:2. Adjust upward or downward based on your exact settings. •Tumor viability: Day 2 pre-doxycycline: 10 to 30% remaining. Day 4: 0 to 10% if mostly cleared earlier, or 20 to 40% if E:T was low and T-cell crash occurred. Controls and variants that de-risk interpretation: •No-doxycycline control to isolate tumor-only dynamics and basal CAR function. •CAR-negative but inducible-CD19 positive T cells to measure bystander fratricide requirement for CAR. •Doxycycline titration to define the safe antigen density window that expands cells without catastrophic fratricide. •Washout of doxycycline at 24 to 48 hours to test reversibility of fratricide and rescue of function. •CRISPR disruption of CD19 cytoplasmic tail in the inducible construct to alter synapse stability and test cis signaling contributions.

A new evaluation shows our AI simulation model, Orb, stands out in many tests. 👏 In this paper (arXiv:2506.01860) by Bowen Han & Yongqiang Cheng, Orb v3 was benchmarked against ~5,000 inorganic crystals and outperformed other MLIPs such as MatterSim and SevenNet-MF-ompa. Take a look at some of the key results: ➡️ Lowest MAE in phonon frequencies (0.50 meV), 30% lower than the next best model. ➡️ Excellent agreement with INS experimental data on cuprates. ➡️ Highest Spearman coeff. (0.956) for phonon density of states. At Orbital, Orb powers real-world products — including our two-phase direct-to-chip cooling solution and dual-use chiller for AI data centers.

@levelsio John von Neumann used to work to extremely loud German marching music on his gramophone which annoyed everyone else at the IAS

Silence is high IQ

Great leaderboards challenge the whole field to move faster. Thanks to all the authors on Matbench Discovery for your contributions!