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Claude made the open-source models that scientists use to predict and design biomolecules faster and more memory-efficient. Claude optimized more than 30 of these models in just under four weeks, speeding them up roughly 4x on average. It also created a low-memory mode that enables the accurate prediction of biomolecular systems larger than 10,000 tokens (amino acids, nucleotides, and atoms from small molecules and i
In this tutorial, we build an ensemble weather forecasting workflow with NVIDIA Earth2Studio. We install the required Earth2Studio components while preserving Colab’s existing CUDA-enabled PyTorch environment, load the FCN prognostic model, and retrieve atmospheric initial conditions from GFS. We then implement a custom wind-power diagnostic that converts 10-meter wind components into turbine capacity factors, along
PhysicsNeMo General Release v2.2.0 Added Adds ShardTensor support for GeoTransolver and FLARE models. Adds zarr save/load for Mesh and DomainMesh via tensordict's zarr storage backend: physicsnemo.mesh.io.to_zarr / from_zarr , with training-appropriate chunking and zstd compression. MeshReader and DomainMeshReader transparently read zarr stores alongside .pmsh / .pdmsh (opt in via pattern ). Requires optional zarr >=
According to Latent Space, since 2022, components of the AI pipeline have been shifting from human-made to model-made, each flip starting with a 'patient zero' at a frontier lab. This trend is characterized as simulation: slightly worse (10%), but 100x cheaper and 10,000x faster. Stage 1 (2022): Reward signal went synthetic with InstructGPT's reward model, followed by Constitutional AI and RLAIF, where AI critiques i
New Nvidia research highlights that for long-horizon tasks, the software harness—the tools, memory management, and rules around a model—matters more than the model itself. Using a custom harness with improved memory handling and a supervisor component, researchers achieved a 100% score on the interactive reasoning benchmark ARC-AGI-3 with Claude Opus 5, compared to 30% without the harness, which was still the best am
NVIDIA's coding agent AVO achieved a perfect score on ARC-AGI-3's 25 public games, solving all 183 levels. Given no rules or stated goals, the agent learns by trial and error, observing outcomes and correcting mistakes. AVO's success hinges on retaining what it has learned.
Bulk materials, as opposed to nanomaterials, require molecular dynamics (MD) simulations on a large spatial scale (~10^9 atoms or more) to adequately capture their atomic-scale physical properties. Previously, the introduction of machine-learning interatomic potentials (MLIPs) has extended MD to this scale, but even single-component bulk systems require tens of thousands of GPUs on high-end supercomputers. However, m
AINews reports on a new paper demonstrating how encrypted reasoning traces from frontier models can be decoded and replayed to other models, sessions, or users, leading to dramatic improvements in open models. Since the release of o1, reasoning models have obscured their traces with cryptographic signatures to prevent distillation, but this study shows that these encrypted blocks can be replayed to weaker models and
Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods - Release v2.1.1 · NVIDIA/physicsnemo
Machine learning (ML) models have become an increasingly important component of the global weather forecasting landscape. Over the past few years at ECMWF, several externally developed models have been run alongside the Integrated Forecasting System (IFS) in an experimental setting, allowing their performance to be assessed against established numerical methods. The upgrade of the IFS to Cycle 50r1 has provided a tes
Scientific progress is often limited not by a lack of new ideas, but by the cost and complexity of testing them. New solutions are needed to make that testing easier—and researchers like Anima Anandkumar are leading the way. She has conducted cutting-edge research across academia and industry for over a decade, pioneering new AI algorithms that simulate physical systems with unprecedented speed and accuracy—in some c
Machine learning interatomic potentials (MLIPs) are transforming the landscape of computational chemistry and materials science. MLIPs enable atomistic simulations that combine the fidelity of computationally expensive quantum chemistry with the scaling power of AI. Yet, developers working at this intersection face a persistent challenge: a lack of robust, Pythonic toolbox for GPU-accelerated atomistic simulation. Fo