GeoVerse Lab
← AI & Computing Sciences Division

AI for Science Center

Pioneers AI methods that accelerate scientific discovery - protein folding, materials design, weather and climate modeling, and mathematical reasoning - through scientific foundation models, physics-informed and equivariant neural networks, and simulation-based inference, while leading cross-division AI4Science programs.

Machine Learning CenterNatural Language Processing CenterComputer Vision CenterHigh-Performance Computing CenterQuantum Computing CenterGenerative AI & Foundation Models CenterReinforcement Learning & Agents CenterAI Safety & Alignment CenterAI for Science CenterRobotics & Embodied AI Center
Research Fields 16
Physics-Informed Neural Networks & Scientific Machine Learning
🧮 Physics-Informed Neural Networks & Scientific Machine Learning
Physics-informed neural networks (PINNs) are neural network surrogates for the solutions o…
Neural Operators & Operator Learning
🌀 Neural Operators & Operator Learning
Neural operator learning trains a neural network to approximate an operator, that is a map…
Protein Structure Prediction & Computational Structural Biology
🧬 Protein Structure Prediction & Computational Structural Biology
Protein structure prediction is the computational task of inferring the three-dimensional …
Materials Discovery & Inverse Materials Design
💎 Materials Discovery & Inverse Materials Design
Materials discovery via artificial intelligence is the use of generative models, graph neu…
AI for Weather & Climate Prediction
🌦️ AI for Weather & Climate Prediction
AI for weather and climate prediction trains graph or transformer neural networks directly…
AI for Genomics & Computational Bioinformatics
🧫 AI for Genomics & Computational Bioinformatics
AI for genomics applies deep sequence models, typically convolutional or transformer archi…
Quantum Chemistry & Molecular Machine Learning
⚛️ Quantum Chemistry & Molecular Machine Learning
Molecular machine learning trains a neural network, typically an equivariant graph neural …
Turbulence & Fluid-Dynamics Surrogate Modelling
🌪️ Turbulence & Fluid-Dynamics Surrogate Modelling
Machine-learning turbulence modelling uses trained neural networks either to replace the s…
Automated Mathematical Discovery & Theorem Proving
📐 Automated Mathematical Discovery & Theorem Proving
Automated mathematical discovery combines learned models, most often trained on human-writ…
AI for Astronomy & Astrophysical Surveys
🔭 AI for Astronomy & Astrophysical Surveys
AI for astronomy applies convolutional neural networks and anomaly-detection algorithms to…
AI-Driven Drug Discovery & Rational Molecular Design
💊 AI-Driven Drug Discovery & Rational Molecular Design
AI-driven drug discovery uses generative neural networks and learned property predictors, …
Simulation-Based Inference & Uncertainty Quantification
🎲 Simulation-Based Inference & Uncertainty Quantification
Simulation-based inference estimates the posterior distribution of unknown parameters of a…
AI for Earth System Science
🌍 AI for Earth System Science
AI for Earth system science trains neural networks, usually on the output of physics-based…
AI-Driven Computational Neuroscience
🧠 AI-Driven Computational Neuroscience
AI-driven computational neuroscience uses artificial neural networks both as explanatory m…
Causal Discovery & Experimental Design for Science
🔗 Causal Discovery & Experimental Design for Science
Causal discovery infers which variables in a dataset causally influence which others, and …
Crystallography & Diffraction-Based Structure Determination AI
🔬 Crystallography & Diffraction-Based Structure Determination AI
AI-assisted crystallography and cryo-electron-microscopy structure determination use train…