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!

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

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from its predecessors, including the red-hot H100 and A100 GPUs. Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. While Nvidia may not spring to mind when thinking of the quant Read more…

2024 Winter Classic: Meet the HPE Mentors

March 18, 2024

The latest installment of the 2024 Winter Classic Studio Update Show features our interview with the HPE mentor team who introduced our student teams to the joys (and potential sorrows) of the HPL (LINPACK) and accompany Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the field was normalized for boys in 1969 when the Apollo 11 missi Read more…

Apple Buys DarwinAI Deepening its AI Push According to Report

March 14, 2024

Apple has purchased Canadian AI startup DarwinAI according to a Bloomberg report today. Apparently the deal was done early this year but still hasn’t been publicly announced according to the report. Apple is preparing Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimization algorithms to iteratively refine their parameters until Read more…

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

March 18, 2024

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

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimizat Read more…

PASQAL Issues Roadmap to 10,000 Qubits in 2026 and Fault Tolerance in 2028

March 13, 2024

Paris-based PASQAL, a developer of neutral atom-based quantum computers, yesterday issued a roadmap for delivering systems with 10,000 physical qubits in 2026 a Read more…

India Is an AI Powerhouse Waiting to Happen, but Challenges Await

March 12, 2024

The Indian government is pushing full speed ahead to make the country an attractive technology base, especially in the hot fields of AI and semiconductors, but Read more…

Charles Tahan Exits National Quantum Coordination Office

March 12, 2024

(March 1, 2024) My first official day at the White House Office of Science and Technology Policy (OSTP) was June 15, 2020, during the depths of the COVID-19 loc Read more…

AI Bias In the Spotlight On International Women’s Day

March 11, 2024

What impact does AI bias have on women and girls? What can people do to increase female participation in the AI field? These are some of the questions the tech Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. 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…

Analyst Panel Says Take the Quantum Computing Plunge Now…

November 27, 2023

Should you start exploring quantum computing? Yes, said a panel of analysts convened at Tabor Communications HPC and AI on Wall Street conference earlier this y Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a 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…

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…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

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…

Training of 1-Trillion Parameter Scientific AI Begins

November 13, 2023

A US national lab has started training a massive AI brain that could ultimately become the must-have computing resource for scientific researchers. Argonne N Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t 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…

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…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire