ORNL Using AI, Big Data Research Tools to Enable Materials Science Discoveries

May 5, 2021

May 5, 2021 — At the Department of Energy’s Oak Ridge National Laboratory, scientists use artificial intelligence, or AI, to accelerate the discovery and development of materials for energy and information technologies.

“AI gives scientists the ability to extract insights from an ever-expanding volume of data,” said David Womble, ORNL’s AI program director. “New AI tools, together with world-class computing capabilities, are critical to maintaining scientific leadership.”

AI uses computers to mine mountains of data for scientific and engineering insights. Starting with high-quality data matters. Well-characterized materials create a strong knowledge foundation for the design of new materials that launch technologies and expand economies. ORNL has a history of materials development dating back to World War II and a rich archive of data generated on world-class instruments by expert researchers. Increasingly, researchers generate high-resolution materials data at a volume, variety and velocity they never before have had to tackle.

“Ten years ago, a Ph.D. student working on steels might analyze five precipitates a day,” said electron microscopist Chad Parish of ORNL. Such precipitates could embrittle an alloy and cause it to fail. “Now we’ve developed a technique that lets us do a thousand precipitates in five hours. We’re drowning in data. AI may hold the key to making the best use of it all.”

Two types of AI help make sense of big data. Machine learning runs algorithms on high-performance computers to find correlations within large data sets and determine how well they match expectations. In doing so, it reveals features that traditional data analyses may miss because they are subtle, infrequent, complex or unexpected. A step further, deep learning models the workings of the human brain (e.g., applying logic and expertise) to distinguish features in data sets that improve discovery, learning and decision making.

“We can now design machines to do the work that once required a human expert, except much faster and on a larger scale,” said ORNL materials scientist Stephen Jesse.

Harnessing machines

David Womble, ORNL’s program director for artificial intelligence, relies on high-performance computing resources like Summit, America’s smartest supercomputer. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

ORNL researchers have stood at the forefront of efforts to harness machines to propel progress in materials science. Starting in 1992, Bobby Sumpter worked on foundational theory and chemical/materials science aspects of machine learning. Markus Eisenbach joined him in creating the machine learning basis for integrating imaging instruments and high-performance computers. They ran theory-based models on supercomputers and validated the results against experimental findings.

In 2001, when the Materials Research Society issued a conference proceeding on AI methods in materials science, ORNL researchers were well represented, advancing methods to analyze, compress and visualize multidimensional data.

At ORNL’s Center for Nanophase Materials Sciences, Sergei Kalinin, a founding member of the American Physical Society topical group on data science, works with colleagues to pioneer automated analysis of growing data from high-resolution microscopy experiments. “We turned to machine learning methods because traditional approaches were not practical or sufficient,” Kalinin said.

Around 2008, ORNL researchers began publishing papers advancing machine learning and deep learning in processing big data from microscopy and tying experimental results to theoretical models. This effort grew over the subsequent decade to include AI advances such as:

  • Complex scanning probe microscope imaging and spectroscopy methods to reveal nanoscale properties in greater detail
  • Complete capture of big data streams from microscope detectors
  • Workflows for on-the-fly analytics of scanning transmission electron microscopy data
  • Automated conversion of microscopy data into libraries of structures and defects
  • Algorithms for learning physical laws from observational data
  • Assistance to tune microscopes, choose regions of interest in samples and control atom-by-atom assembly

“We are still just scratching the surface with the use of deep learning for quantitative structural analysis of microscopy data,” ORNL’s Albina Borisevich said. “If we can transition from isolated problems to a more general approach, it can completely revolutionize the field.”

For example, ORNL researchers Wei-Ren Chen and Changwoo Do at the Spallation Neutron Source use machine learning to assist in small-angle neutron scattering characterization of a wide range of material structures. The machine learning methods may help them suggest models for data analysis.

ORNL researchers such as Suhas Somnath also have investigated ways to share data widely. He scales codes to run on distributed computing architectures and develops data infrastructure solutions.

“Continual advancements in automation, computational power, and resolution and speed of detectors in instruments now result in ever larger, numerous, more diverse and complex data sets from both simulations and experiments,” said Somnath. “DataFed and the CADES Data Gateway will imminently facilitate collaborative collection, curation, annotation and sharing of data.”

The Summit supercomputer at the Oak Ridge Leadership Computing Facility is ideally suited for training and deployment of AI algorithms on large data sets owing to its 27,648 state-of-art graphics processing units, high-speed file system and large memory. A recent materials microscopy application demonstrated AI scaled to use all of Summit while running at 93% efficiency.

Quality in, quality out

“The major focus in AI tends to be on data analytics, but we should emphasize that the data itself is important,” said ORNL materials scientist Dongwon Shin, who runs thermodynamic models on supercomputers to design high-performance alloys.

He said the ORNL advantage is akin to “grandma knowledge.” You may follow a cookie recipe to the letter, but your grandmother — with her in-depth knowledge of ingredient interactions, etc. — will out-bake you every time. Likewise, ORNL researchers who have worked on materials for decades have world-class data sets with detailed pedigrees.

Shin realized that most machine learning tools were developed by and for programming experts, not the domain scientists. His team developed an open-source toolkit called ASCENDS that lets scientists with little knowledge of programming or data science apply data analytics as easily as using Excel. ASCENDS analyzes correlations between input features and target properties to facilitate the generation and validation of hypotheses and training of machine learning models that predict materials behavior.

Visualizing material success

Visualizing big data is an additional challenge. Materials scientists often use software that comes with the instruments they buy. “Much of the vendor software presents the data collected by instruments in a bad way,” said ORNL’s Philip Edmondson, who investigates materials for nuclear fission and fusion applications.

The scientific community is clamoring for open-source software to help turn big data into something the human mind can interpret. Edmondson and Parish have recommended best practices for improving data visualization.

Materials for advanced nuclear reactors are irradiated in ORNL’s High Flux Isotope Reactor. Then scientists characterize the specimens in detail, and machine learning methods analyze the measurements to determine how irradiation changes the microstructures and properties that are likely to affect the lifetimes of fission or fusion energy systems. “With nuclear materials, there might be millions of dollars and five or more years of investment behind getting one three-millimeter sample into the electron microscope,” Parish explained. “You want to make sure that you’re gleaning all of the scientific insight you can from that sample.”

“We’re investing a lot of money and time into collecting good data,” Edmondson said. “Let’s understand it.”

UT-Battelle manages ORNL for the Department of Energy’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. The Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.


Source: ORNL

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Under The Wire: Nearly HPC News (June 13, 2024)

June 13, 2024

As managing editor of the major global HPC news source, the term "news fire hose" is often mentioned. The analogy is quite correct. In any given week, there are many interesting stories, and only a few ever become headli Read more…

Quantum Tech Sector Hiring Stays Soft

June 13, 2024

New job announcements in the quantum tech sector declined again last month, according to an Quantum Economic Development Consortium (QED-C) report issued last week. “Globally, the number of new, public postings for Qu Read more…

Labs Keep Supercomputers Alive for Ten Years as Vendors Pull Support Early

June 12, 2024

Laboratories are running supercomputers for much longer, beyond the typical lifespan, as vendors prematurely deprecate the hardware and stop providing support. A typical supercomputer lifecycle is about five to six years Read more…

MLPerf Training 4.0 – Nvidia Still King; Power and LLM Fine Tuning Added

June 12, 2024

There are really two stories packaged in the most recent MLPerf  Training 4.0 results, released today. The first, of course, is the results. Nvidia (currently king of accelerated computing) wins again, sweeping all nine Read more…

Highlights from GlobusWorld 2024: The Conference for Reimagining Research IT

June 11, 2024

The Globus user conference, now in its 22nd year, brought together over 180 researchers, system administrators, developers, and IT leaders from 55 top research computing centers, national labs, federal agencies, and univ Read more…

Nvidia Shipped 3.76 Million Data-center GPUs in 2023, According to Study

June 10, 2024

Nvidia had an explosive 2023 in data-center GPU shipments, which totaled roughly 3.76 million units, according to a study conducted by semiconductor analyst firm TechInsights. Nvidia's GPU shipments in 2023 grew by more Read more…

Under The Wire: Nearly HPC News (June 13, 2024)

June 13, 2024

As managing editor of the major global HPC news source, the term "news fire hose" is often mentioned. The analogy is quite correct. In any given week, there are Read more…

Labs Keep Supercomputers Alive for Ten Years as Vendors Pull Support Early

June 12, 2024

Laboratories are running supercomputers for much longer, beyond the typical lifespan, as vendors prematurely deprecate the hardware and stop providing support. Read more…

MLPerf Training 4.0 – Nvidia Still King; Power and LLM Fine Tuning Added

June 12, 2024

There are really two stories packaged in the most recent MLPerf  Training 4.0 results, released today. The first, of course, is the results. Nvidia (currently Read more…

Highlights from GlobusWorld 2024: The Conference for Reimagining Research IT

June 11, 2024

The Globus user conference, now in its 22nd year, brought together over 180 researchers, system administrators, developers, and IT leaders from 55 top research Read more…

Nvidia Shipped 3.76 Million Data-center GPUs in 2023, According to Study

June 10, 2024

Nvidia had an explosive 2023 in data-center GPU shipments, which totaled roughly 3.76 million units, according to a study conducted by semiconductor analyst fir Read more…

ASC24 Expert Perspective: Dongarra, Hoefler, Yong Lin

June 7, 2024

One of the great things about being at an ASC (Asia Supercomputer Community) cluster competition is getting the chance to interview various industry experts and Read more…

HPC and Climate: Coastal Hurricanes Around the World Are Intensifying Faster

June 6, 2024

Hurricanes are among the world's most destructive natural hazards. Their environment shapes their ability to deliver damage; conditions like warm ocean waters, Read more…

ASC24: The Battle, The Apps, and The Competitors

June 5, 2024

The ASC24 (Asia Supercomputer Community) Student Cluster Competition was one for the ages. More than 350 university teams worked for months in the preliminary competition to earn one of the 25 final competition slots. The winning teams... Read more…

Atos Outlines Plans to Get Acquired, and a Path Forward

May 21, 2024

Atos – via its subsidiary Eviden – is the second major supercomputer maker outside of HPE, while others have largely dropped out. The lack of integrators and Atos' financial turmoil have the HPC market worried. If Atos goes under, HPE will be the only major option for building large-scale systems. Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Everyone Except Nvidia Forms Ultra Accelerator Link (UALink) Consortium

May 30, 2024

Consider the GPU. An island of SIMD greatness that makes light work of matrix math. Originally designed to rapidly paint dots on a computer monitor, it was then Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Some Reasons Why Aurora Didn’t Take First Place in the Top500 List

May 15, 2024

The makers of the Aurora supercomputer, which is housed at the Argonne National Laboratory, gave some reasons why the system didn't make the top spot on the Top Read more…

Leading Solution Providers

Contributors

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

The NASA Black Hole Plunge

May 7, 2024

We have all thought about it. No one has done it, but now, thanks to HPC, we see what it looks like. Hold on to your feet because NASA has released videos of wh Read more…

GenAI Having Major Impact on Data Culture, Survey Says

February 21, 2024

While 2023 was the year of GenAI, the adoption rates for GenAI did not match expectations. Most organizations are continuing to invest in GenAI but are yet to Read more…

Google Announces Sixth-generation AI Chip, a TPU Called Trillium

May 17, 2024

On Tuesday May 14th, Google announced its sixth-generation TPU (tensor processing unit) called Trillium.  The chip, essentially a TPU v6, is the company's l Read more…

Intel’s Next-gen Falcon Shores Coming Out in Late 2025 

April 30, 2024

It's a long wait for customers hanging on for Intel's next-generation GPU, Falcon Shores, which will be released in late 2025.  "Then we have a rich, a very Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

Intel Plans Falcon Shores 2 GPU Supercomputing Chip for 2026  

August 8, 2023

Intel is planning to onboard a new version of the Falcon Shores chip in 2026, which is code-named Falcon Shores 2. The new product was announced by CEO Pat Gel Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire