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Research Initiative
AI + Science
A new paradigm of transformational AI-enabled scientific discovery across the physical and biological sciences, advancing core AI principles and training a new generation of diverse interdisciplinary scientists.

Overview

Artificial intelligence (AI) and machine learning (ML) will fundamentally change the nature and pace of scientific discovery. Widespread adoption of AI in the sciences has the potential to integrate scientific inquiry with modes of hypothesis generation, data analysis, simulation, and testing that will transform our capacity to address scientific problems that currently appear intractable.

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People and Partners

People

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Pritzker AI+Science Joint Initiative with CalTech

A conference that brings together an elite and diverse cohort of leading researchers in core AI and domain sciences to lead conversations and drive partnerships that will shape future inquiry, industry investment, and entrepreneurial opportunities.
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The Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a Program of Schmidt Sciences

Training scholars who seek to advance and accelerate the adoption of artificial intelligence (AI) in the natural sciences
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AI+Science Schmidt Fellows Seminar Series

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Schmidt Faculty Fellows

A program that supports faculty at Nigerian universities interested in applying AI methodology to address significant research questions in the natural sciences and engineering.
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Summer School

The goal of this yearly program is to introduce a new generation of diverse interdisciplinary graduate students and researchers to the emerging field of AI+Science.
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SkAI

The NSF-Simsons AI Institute for the Sky(SkAI) will synergistically accelerate Astro-AI research and help educate a diverse Astro-AI workforce.
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NITMB

The NSF-Simons National Institute for Theory and Mathematics in Biology (NITMB) integrates the disciplines of mathematics and biology to transform the practice of biological research and inspire new mathematical discoveries.
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Center for Living Systems

A National Science Foundation Physics Frontier Center
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Projects

Developing Artificial Curiosity Algorithms

Discovering the Design Rules Linking Protein Sequence to Function

Machine Learning Models for Predicting Heart Disease Progression

Building Cyberinfrastructure for Rare-Event Searches

Using Field-Effect Transistor Array to Monitor Water Quality

On-Chip Machine Learning for intelligent Pixel Detectors

Improving Batteries to Combat Climate Change

Automating Electron Microscopy with AI

Improving Prediction of Breast Cancer Patient Response to Therapy

Predicting Material Properties

Examining The Mystery of How Living Organisms Develop

Advancing Molecular Measurement with Machine Learning

Creating Microbiomes That Improve Human and Environmental Health
A structure called a Sidestream Elevated Pool Aeration station in the Chicagoland Cal-Sag Channel and Calumet River is intended to aerate the river water, which protects fish populations and eliminates odors. Credit: Metropolitan Water Reclamation District

AI-Assisted Molecular Engineering to Detect and Remove Water Contaminants

Learned Emulators of Physics Simulations

Real-Time Adaptive Deep Learning for Discovery Science

Artificially Intelligent Electrochemistry

Automated Experimental Design for Cosmic Discovery

Is Climate Change Changing Clouds?

Neural Network Algorithms to Decode the Octopus Neural Network

Rational Protein Engineering using Data-Driven Generative Models

Related Insights, News, and Past Events

Campus NewsSep 18, 2024

NSF and Simons Foundation launch $20 million National AI Research Institute in Astronomy

Fig: Formation of HIV capsid simulated by a coarse-grained model. From: https://www.google.com/url?q=https://www.science.org/doi/epdf/10.1126/sciadv.add7434&sa=D&source=docs&ust=1715014663521469&usg=AOvVaw3fATTBC_yAvxPv6WPYI17_
BlogMay 22, 2024

AI as a Great Teacher for Molecular Dynamic Modeling

DSI NewsMay 22, 2024

Eric and Wendy Schmidt AI in Science Postdoctoral Fellows host first-ever hackathon

DSI NewsMay 17, 2024

AI+Science Conference receives funding to expand across the next three years

AI methods and models have enabled a huge leap in our understanding of how images are processed in the brain. We used to describe visual neurons as “edge detectors” and “face detectors”. Using deep neural networks, we have discovered that images like these (which really can’t be described with words) are richer models of single neurons in our visual system. I liken these AI-enabled descriptions of neural function, perhaps ironically, to a whole new kind of vocabulary that neuroscientists can now use to explain the visual system. (Images from various papers including a,b, c, d, e , f)
BlogMay 16, 2024

Expanding Our Vocabulary of Vision Using AI

Caption: (left) We want to find networks which can easily switch between two different motions. (right) Alternating the design process for each motion results in networks which can easily switch between the two motions.
BlogMay 09, 2024

Teaching materials to adapt

Fig: The neural network will be trained on a dataset generated by validated quantum mechanics methods on small molecules. The model will then be used to predict the x-ray damage effect in more complex systems.
BlogMay 02, 2024

From Protein Structures to Clean Energy Materials to Cancer Therapies: Using AI to Understand and Exploit X-ray Damage Effects

Figure 1: Aerial view of the 27-kilometer-long Large Hadron Collider (LHC) located on the border of France and Switzerland near Geneva. The LHC collides particles at nearly the speed of light to study the universe in a controlled experimental facility. The Higgs boson was discovered with the LHC in 2012. Image credit to ESO Supernova.
BlogApr 25, 2024

Towards New Physics at Future Colliders: Machine Learning Optimized Detector and Accelerator Design

Figure 1. A typical voltage sensor (left) for a membrane protein (right) that has multiple voltage sensing domains.
BlogApr 18, 2024

Uncovering Patterns in Structure for Voltage Sensing Membrane Proteins with Machine Learning

BlogApr 11, 2024

Finding the likely causes when potential explanatory factors look alike

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