CMU Scientists Use XSEDE-Allocated Resources to Simulate Improved Battery Components

July 11, 2019

July 11, 2019 — The move toward cleaner, cheaper energy would be much easier if we had more powerful, safer battery technologies. Carnegie Mellon University (CMU) scientists have used the XSEDE-allocated Bridges system at the Pittsburgh Supercomputing Center (PSC) and Comet at the San Diego Supercomputer Center (SDSC) to simulate new battery component materials that are inherently safer and more powerful than currently possible.

One of the predicted new low cobalt structures of Li Nix Mn y Co 1-x-y O2 with a ratio of nickel to maganese to cobalt of 18:5:1. The nickel is shown in grey, the maganese in magenta and the cobalt in blue. The lithium layer is shown in green and oxygen in red. Image courtesy of Pittsburgh Supercomputing Center. 

Why It’s Important:

Better batteries might not make all our energy problems vanish. But they’d be a really good start. Companies as varied as Tesla, Chevrolet, Jaguar and Audi have begun selling electric cars. This promises a generation of vehicles that run on whatever technology at a given time provides the most economical—and cleanest—electricity. But the performance of today’s batteries falls short for use in larger vehicles, such as trucks and aircraft. By the same token, wind and, increasingly, solar power are becoming important actors in the U.S. energy grid. But they’d be far more cost-efficient if we could store the peak power they generate during the day, so that it can be used whenever needed. Again, today’s batteries aren’t quite up to it.

“Many companies are moving toward personal vehicles being electrified. Moving to larger vehicles such as trucks or aviation requires a higher energy density; and as we approach higher energy densities, the technical problems become bigger,” said Gregory Houchins, CMU.

There’s a gap between what today’s battery technology can do and what’s needed for these transformations. Batteries’ ability to store energy—their “energy density”—has to increase, and it has to happen without risk of fires, as seen in some devices. It would also be nice if these batteries didn’t contain so much cobalt, which is found in very few parts of the world and so increases their cost.

“We’re trying to find new solid electrolytes that can conduct ions quickly as [today’s] liquid electrolytes, which are flammable. And we need anodes with a very high energy density,” said Zeeshan Ahmad, CMU.

Graduate students Zeeshan Ahmad and Gregory Houchins, working in the CMU lab of Assistant Professor Venkat Viswanathan, have been pursuing different avenues in their group’s quest to find safer, more powerful solid-state lithium batteries. To do this, they turned to simulations and machine learning on two XSEDE-allocated systems: Bridges at PSC and Comet at SDSC.

How XSEDE Helped:

Houchins worked on the problem of finding cathodes—the positive pole of a battery—that contain lower amounts of expensive cobalt. He wrote software that randomly explores different cathode compositions and tests their efficiencies using the known properties of each simulated material’s components. This would have been a problem on a traditional supercomputer. That’s because the two steps of generating a candidate material and simulating its properties require repeatedly refining the application. A traditional system would have forced him to perform both steps for each material and wait to see if they worked properly—or failed, in which case he had to start again.

“One thing I like about Bridges is the interactive feature … I’ve written a code that will sample the composition space randomly, and from that read-in try and find the best model. It’s all automated, and debugging is difficult to do [in a single submitted batch to a traditional supercomputer]. I’m able to use the interactive mode to quickly debug that code,” said Gregory Houchins, CMU.

Bridges, though, emphasizes interactive access. That feature allowed Houchins to monitor the computation’s progress as it happened, and correct where needed. This helped him to debug quickly, wasting far less time. To date his software has identified over a dozen alternative cathodes predicted to perform as well as the high cobalt-containing materials now used in lithium batteries.

Ahmad, meanwhile, was working on another problem. Batteries consist of a positive cathode, a negative anode, and an electrolyte that allows electricity to flow between them. Lithium batteries are powerful and compact. But their liquid electrolytes are highly flammable. Also, the tendency of dendrites—literally, “little fingers”—to form on the anode and reach toward the cathode further risks fire by causing a short circuit between the anode and cathode. It also limits the lifetime of the battery.

“XSEDE has provided my group with the computational resources needed to tackle some of these very important problems,” said Venkat Viswanathan, CMU.

Ahmad and his collaborators used PSC’s Bridges and SDSC’s Comet to simulate non-flammable solid electrolytes that also wouldn’t allow dendrites to form on the anode. This project used machine learning, a type of artificial intelligence that makes the computer experiment with many random solutions until it finds ones that meet the goal. The scientists screened almost 13,000 candidate solid electrolytes, finding 10 predicted to discourage dendrite formation. They reported these results in the journal ACS Central Science last year.

Both projects need further development—both will screen for more candidates, and the materials identified need to be made and tested in the real world to confirm they have the predicted properties. But they’ve taken the first steps in cracking the fundamental problems that limit battery storage.

Deeper Dive: Machine Learning on Multiple Systems

Zeeshan Ahmad’s project using machine learning to predict whether anode materials would be likely to form dendrites had two major components, each of which required a different computer architecture to run well. The first method, used by Ahmad’s coauthor Tian Xie of Massachusetts Institute of Technology (MIT), was a convolutional neural network. CNNs consist of layers of analysis in which parts of the computation are programmed to behave roughly like nerve cells in the brain’s optical cortex, with each virtual “neuron” connecting with a set of neurons in the layer above and below it.

In the collaborators’ CNN, the neural network focused on two major properties of the materials—the shear modulus, or resistance against a shearing force, and the bulk modulus, or the resistance against compression. These properties determine whether dendrites will form in a battery using that material as the electrolyte. The CNN has good predictive power for these two properties since the training data are accurate and well established from first-principles.

But the candidate materials are anistropic—their properties vary in different directions. Because of this, the shear and bulk modulus are not enough to determine whether a material will form dendrites. To test the different anisotropic properties of each material, Ahmad used different regression techniques suited to the available data. The types of regression that worked best for these computations were gradient boosting and kernel ridge regression.

“We’re running calculations that have to store a lot of data on the quantum mechanical wavefunction of electron in different systems … the stored data increase to the third power of the number of electrons in the system. Bridges’ high memory per core enables us to use just one node; in another system, we would have to use multiple nodes—besides the data transfer reason, in conventional Slurm queuing systems, it would take more time for a multiple-node job to clear the queue,” said Zeeshan Ahmad, CMU.

GPUs, or graphics processing units, tend to make CNN computations run fastest. But the quantum mechanical calculations underlying the computation are extremely memory hungry. At the time, the GPU resources available would have been overcome by a roadblock in accessing the data. So each computation in effect required a different kind of computer.

The scientists ran the CNN on the GPU nodes in SDSC’s Comet, and the regression on the “regular memory” CPU nodes in PSC’s Bridges. The latter had enough memory—128 gigabytes, enough to qualify as large-memory on most HPC systems—to speed the regression and quantum mechanical computations. The two systems helped the scientists to run both computations quickly and efficiently. Since this phase of the research concluded, a new GPU-AI resource has been added to Bridges, including a DGX-2 node that will enable the entire workflow. The MIT group is continuing their research on this resource, which will make future such computations even faster and more efficient.


Source: Ken Chiacchia, Pittsburgh Supercomputing Center

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!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues 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 power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion 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…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen 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…

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…

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…

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…

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…

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…

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…

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

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…

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…

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…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire