STRATA is the first autoregressive transformer emulator for global 4.9-km storm-resolving atmospheric dynamics, achieving 50x better energy efficiency than the underlying physics model while producing realistic km-scale features in 24-hour forecasts.
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6 Pith papers cite this work, alongside 44 external citations. Polarity classification is still indexing.
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AtomWorld enables the first direct atomistic simulation of RPV steel at year-and-meter scales, handling ten-quintillion-atom systems and simulating one service year in 1.71 days with 92-97% scaling efficiency on leadership supercomputers.
SOAP and SOAP-Muon optimizers deliver faster convergence and higher final accuracy than Adam for NequIP and Allegro MLIPs, with the largest gains under partial force supervision.
MALOQ introduces a scalable SO(2)-equivariant ML framework with custom kernels and edge-wise graph distribution for predicting large-scale quantum transport operators.
SMC-AI scales Monte Carlo simulations to 4 trillion atoms on AI hardware clusters, achieving 32 times larger systems and 1.3 times higher throughput than prior records while decoupling ML models from the simulation core.
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
citing papers explorer
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Scaling Storm-Resolving Atmospheric AI Simulation to the Entire Planet
STRATA is the first autoregressive transformer emulator for global 4.9-km storm-resolving atmospheric dynamics, achieving 50x better energy efficiency than the underlying physics model while producing realistic km-scale features in 24-hour forecasts.
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Unfolding an Atomistic World: Atomistic Simulation of Reactor Pressure Vessel Steel Across Year-and-Meter Scales
AtomWorld enables the first direct atomistic simulation of RPV steel at year-and-meter scales, handling ten-quintillion-atom systems and simulating one service year in 1.71 days with 92-97% scaling efficiency on leadership supercomputers.
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Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials
SOAP and SOAP-Muon optimizers deliver faster convergence and higher final accuracy than Adam for NequIP and Allegro MLIPs, with the largest gains under partial force supervision.
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MALOQ: Massively Accelerated Learning of Operators for Quantum Transport
MALOQ introduces a scalable SO(2)-equivariant ML framework with custom kernels and edge-wise graph distribution for predicting large-scale quantum transport operators.
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SMC-AI: Scaling Monte Carlo Simulation to Four Trillion Atoms with AI Accelerators
SMC-AI scales Monte Carlo simulations to 4 trillion atoms on AI hardware clusters, achieving 32 times larger systems and 1.3 times higher throughput than prior records while decoupling ML models from the simulation core.
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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.