Michigan Technological University researchers are involved in three proposals tapped
by the U.S. Department of Energy for consideration under its recently launched Genesis
Mission: Transforming Science and Energy with AI. The mission’s goal is to address
national science and technology challenges using artificial intelligence
The U.S. Department of Energy (DOE) selected one Michigan Tech-led research proposal
and two others involving researchers in the University’s Department of Physics for award negotiations related to the Genesis Mission challenge “Predicting U.S.
Water for Energy.”
“The Genesis Mission is the premier federal program advancing artificial intelligence
in science,” said Andrew Barnard, Michigan Tech’s vice president for research. “Having Michigan Tech researchers lead
and participate in Genesis projects reflects Michigan Tech as a premier R1 research
institution with world-renowned faculty at the peak of their respective fields.”
The Genesis Mission brings together researchers, agency heads, Congressional members,
scientific leaders and strategic industry partners to investigate solutions to 26
unique national science and technology challenges using artificial intelligence. The
Genesis Mission is a historic national initiative led by the U.S. Department of Energy,
which is building the world’s most powerful integrated science discovery platform.
By uniting government, industry, academia and philanthropy, it is accelerating breakthroughs
in energy, scientific discovery and national security through a new platform that
combines AI, supercomputing, quantum systems and advanced scientific instruments.
The mission is currently in phase one, the goal of which is to identify promising
pathways toward transformative scientific capabilities and establish a foundation
for future investment and scale. Project teams will design and demonstrate research
workflows that integrate AI with scientific investigation, while rigorously evaluating
whether those approaches can accelerate discovery, improve predictive capabilities,
enhance experimentation or generate new scientific insights.
Using Tech’s Pi Cloud Chamber to Predict Water Resources
Water availability is essential for expanding energy production, as well as for preserving
and protecting the nation’s health and security. The Genesis Mission challenge “Predicting
U.S. Water for Energy” seeks to address fundamental scientific gaps in scientists’
understanding of terrestrial and atmospheric systems that limit the nation’s ability
to predict water resources, especially on the time scale of weeks to years. The vision
is creating AI capable of multiscale temporal reasoning that could tackle three interrelated
grand challenges: cloud physics, surface and subsurface water flows, and the broader
hydrologic cycle.
Michigan Tech’s Raymond Shaw, university professor of physics, is the principal investigator
on the University’s proposal “AI-accelerated exploration of droplet collision-coalescence
using observation constrained, multiscale modeling of a turbulent-convection cloud
chamber.” The project proposes a two-stage, AI-enabled data-driven framework using
the unique, dynamic steady-state of a convection cloud chamber.
Shaw and his team of researchers intend to sample droplet size distribution fluctuations
over time in Michigan Tech’s Pi Convection Cloud Chamber and extract fundamental microphysical
parameters from the data. The information will be used to construct an efficient,
reduced-order predictive model to anticipate when and how precipitation will develop,
which affects water supply. The project aims to offer a comprehensive pathway for
advancing next-generation cloud microphysics parameterizations. Project partners include
Brigham Young University, Brookhaven National Laboratory (BNL), the National Center
for Atmospheric Research (NCAR), Pacific Northwest National Laboratory (PNNL) and
the University of Utah.
“One of our underlying philosophies is that machine learning and AI-accelerated scientific
discovery will be most successful when guided by the combination of known physics
and high-quality data,” said Shaw.
AI is only as good as its training data. In this team’s case, high-quality data comes
from the Pi Cloud Chamber, providing high-fidelity training in circumstances not currently
feasible from field measurements. This combination of measurements can also benchmark
the computationally expensive numerical models, guiding them in the kinds of training
that they can provide for the AI models.
“We have a good idea of what the physics of precipitation formation should look like,
but there are large uncertainties in certain important bottlenecks in the formation
process,” said Shaw. “By focusing on those bottlenecks, we can improve our understanding
of physics, which in turn can impact the quality of computer models used for real-world
decisions about water and energy infrastructure.”
AI for better water resource prediction and energy development.
Shaw is also co-principal investigator on Brookhaven National Laboratory’s Genesis Mission proposal “An Automated, Multimodal-AI-Enabled Cloud Chamber for Constraining
Cloud Microphysical Processes in Earth System Models.” BNL’s objective is to develop
an embodied AI cloud chamber control system powered by a large language model to demonstrate
the advantages of using AI in laboratory experiments, such as setting up a specific
target, controlling experimental conditions and showing measurable performance gains.
Shaw has been collaborating with colleagues at BNL for several years as they’ve developed
a cloud chamber to test technologies necessary as stepping stones toward the eventual
construction of a large convection-cloud chamber for exploring clouds that form rain
and snow. Tech’s Pi Cloud Chamber operation is already complex, and in a similar system
on a larger scale, the complexity can become overwhelming.
“It’s a perfect opportunity for AI to play a role in allowing scientists to focus
on the science, rather than details of wall-temperature configurations and other factors,”
said Shaw. “In much the same way as a self-driving car allows the driver to provide
a destination and not worry about each detailed turn or traffic structure in getting
there, this envisioned system will allow scientists to formulate hypotheses and request
desired cloud conditions, without having to find the necessary settings by trial and
error.”
Will Cantrell, Michigan Tech’s associate provost, graduate school dean and physics professor, is
co-principal investigator for Pacific Northwest National Laboratory’s Genesis Mission proposal “Earth-Atmosphere Agentic Research and Learning — Physics-Constrained
AI Closure Development for Cloud Microphysics and Turbulence.” The project’s overall
objective is to develop and demonstrate a physics-constrained, AI-enabled cloud microphysics
closure for the Energy Research and Forecasting Model (ERF). The ERF model is a new
solver for predicting mesoscale and microscale dynamics in the atmosphere, designed
for emerging high-performance computing architectures. PNNL’s project hopes to capture
the effects of unresolved fluctuations in water vapor concentrations on condensational
growth and evaporation of cloud droplets, and to establish a human-supervised agentic
workflow for rapid closure development, implementation and evaluation.
“We don’t know the whole chain of events that lead from cloud formation to precipitation,”
said Cantrell. “However, we do know that when a small subset of cloud droplets reach
a certain size threshold, that rain is more likely, but we can’t quite pin down yet
how that happens. We will tackle that problem using careful laboratory experiments
at Tech, coupled with PNNL’s insights on what AI can provide when fed that data.”
All three project proposals are currently in award negotiations with the DOE. Award
announcements are expected in the coming months. Shaw attended the Genesis Mission
Annual Summit on July 22, and is enthusiastic about his part in developing solutions
to these long-standing science challenges. If the projects are successful, he believes
they could radically improve the nation’s ability to anticipate water supply in the
context of changing water availability, demands, energy technologies and ambitions
for energy expansion.
“It’s an honor to be part of the inaugural teams in the Genesis Mission, and exciting
to have this opportunity to further explore the physics of precipitation formation,”
said Shaw. “Our collaboration draws from a wide range of scientific backgrounds. We’re
looking forward to working together to develop AI-ready datasets and machine learning
and AI tools that will help accelerate progress on a challenging science problem that
is highly relevant to society.”
Michigan Technological University is an R1 public research university founded in 1885 in Houghton, and is home to nearly 7,500 students from more than 60 countries around the world. Consistently ranked among the best universities in the country for return on investment, Michigan’s flagship technological university offers more than 185 undergraduate and graduate degree programs in science, technology, engineering, mathematics, computing, forestry, business, health professions, robotics, psychology, social sciences, humanities, and the arts. The rural campus is situated just miles from Lake Superior in Michigan’s Upper Peninsula, offering year-round opportunities for outdoor adventure.
