Back to Table of contents

Primeur weekly 2021-01-11

Exascale supercomputing

Preparing an earthquake risk assessment application for exascale ...

Quantum computing

A bit too much: reducing the bit width of Ising models for quantum annealing ...

The world's first integrated quantum communication network ...

Focus on Europe

GBP 20 million funding boost for science supercomputer will drive science simulation and UK-wide innovation ...

Northern Data acquires data centre site in Northern Sweden fully powered by green energy ...

Research and Markets to issue report on High Performance Computing (HPC) Market by Component, Deployment Type, Organization Size, Server Prices Band, Application Area, and Region - Global Forecast to 2025 ...

Environmental researchers benefit from powerful supercomputer at Plymouth Marine Laboratory ...

Middleware

XSEDE welcomes new service providers ...

Hardware

IBM appoints Gary D. Cohn as Vice Chairman ...

IBM appoints Martin Schroeter as CEO of "NewCo" independent managed infrastructure services business to spin out from IBM ...

Light-based processors boost machine-learning processing ...

E4 Computer Engineering announces University of Pisa as the first customer of Ultrafast Storage, Totally Integrated (USTI), the new solution for high performance distributed block storage ...

Existing Northern Data bitcoin mining customer expands contract volume by more than 200 MW ...

Power XL Pro launched as new professional server based on AMD EPYC technology ...

Swinburne-led research team demonstrates world's fastest optical neuromorphic processor ...

Applications

Supercomputer models describe chloride's role in corrosion ...

HPC-AI Advisory Council to host HPC AI AC Conference in Japan on January, 26 ...

Insights through atomic simulation ...

Advanced materials in a snap ...

New data-driven global climate model provides projections for urban environments ...

Frequency data for stable power supply ...

Physicists observe competition between magnetic orders ...

UTSA Artificial Intelligence Consortium receives over $1 million in research funding ...

Entangling electrons with heat ...

NIO partners with NVIDIA to develop a new generation of automated driving electric vehicles ...

Navantia leverages Ansys' digital transformation solutions to design next-gen naval vessels ...

Engineering graduate student places second in international research competition ...

The Cloud

Covid-19 genome sequencing project gets major upgrade ...

IBM provides Harris-Stowe State University with $2 million in Artificial Intelligence and open hybrid Cloud technology resources to help students build modern skills ...

IBM and Avertra collaborate to drive digital transformation for energy & utilities clients with IBM Cloud ...

UJET CCaaS Cloud contact centre now available on Oracle Cloud Marketplace ...

Advanced materials in a snap


Sandia National Laboratories has developed a machine learning algorithm capable of performing simulations for materials scientists nearly 40.000 times faster than normal. Credit: Eric Lundin, Sandia National Laboratories.
5 Jan 2021 Albuquerque - If everything moved 40.000 times faster, you could eat a fresh tomato three minutes after planting a seed. You could fly from New York to Los Angeles in half a second. And you'd have waited in line at airport security for that flight for 30 milliseconds.

Thanks to machine learning, designing materials for new, advanced technologies could accelerate that much.

A research team at Sandia National Laboratories has successfully used machine learning - computer algorithms that improve themselves by learning patterns in data - to complete cumbersome materials science calculations more than 40.000 times faster than normal.

Their results, published January 4 in npj Computational Materials , could herald a dramatic acceleration in the creation of new technologies for optics, aerospace, energy storage and potentially medicine while simultaneously saving laboratories money on computing costs.

"We're shortening the design cycle", stated David Montes de Oca Zapiain, a computational materials scientist at Sandia who helped lead the research. "The design of components grossly outpaces the design of the materials you need to build them. We want to change that. Once you design a component, we'd like to be able to design a compatible material for that component without needing to wait for years, as it happens with the current process."

The research, funded by the U.S. Department of Energy's Basic Energy Sciences program, was conducted at the Center for Integrated Nanotechnologies, a DOE user research facility jointly operated by Sandia and Los Alamos national labs.

Sandia researchers used machine learning to accelerate a computer simulation that predicts how changing a design or fabrication process, such as tweaking the amounts of metals in an alloy, will affect a material. A project might require thousands of simulations, which can take weeks, months or even years to run.

The team clocked a single, unaided simulation on a high-performance computing cluster with 128 processing cores - a typical home computer has two to six processing cores - at 12 minutes. With machine learning, the same simulation took 60 milliseconds using only 36 cores-equivalent to 42.000 times faster on equal computers. This means researchers can now learn in under 15 minutes what would normally take a year.

Sandia's new algorithm arrived at an answer that was 5% different from the standard simulation's result, a very accurate prediction for the team's purposes. Machine learning trades some accuracy for speed because it makes approximations to shortcut calculations.

"Our machine-learning framework achieves essentially the same accuracy as the high-fidelity model but at a fraction of the computational cost", stated Sandia materials scientist Rémi Dingreville, who also worked on the project.

Rémi Dingreville and David Montes de Oca Zapiain are going to use their algorithm first to research ultrathin optical technologies for next-generation monitors and screens. Their research, though, could prove widely useful because the simulation they accelerated describes a common event - the change, or evolution, of a material's microscopic building blocks over time.

Machine learning previously has been used to shortcut simulations that calculate how interactions between atoms and molecules change over time. The published results, however, demonstrate the first use of machine learning to accelerate simulations of materials at relatively large, microscopic scales, which the Sandia team expects will be of greater practical value to scientists and engineers.

For instance, scientists can now quickly simulate how miniscule droplets of melted metal will glob together when they cool and solidify, or conversely, how a mixture will separate into layers of its constituent parts when it melts. Many other natural phenomena, including the formation of proteins, follow similar patterns. And while the Sandia team has not tested the machine-learning algorithm on simulations of proteins, they are interested in exploring the possibility in the future.

Source: DOE/Sandia National Laboratories

Back to Table of contents

Primeur weekly 2021-01-11

Exascale supercomputing

Preparing an earthquake risk assessment application for exascale ...

Quantum computing

A bit too much: reducing the bit width of Ising models for quantum annealing ...

The world's first integrated quantum communication network ...

Focus on Europe

GBP 20 million funding boost for science supercomputer will drive science simulation and UK-wide innovation ...

Northern Data acquires data centre site in Northern Sweden fully powered by green energy ...

Research and Markets to issue report on High Performance Computing (HPC) Market by Component, Deployment Type, Organization Size, Server Prices Band, Application Area, and Region - Global Forecast to 2025 ...

Environmental researchers benefit from powerful supercomputer at Plymouth Marine Laboratory ...

Middleware

XSEDE welcomes new service providers ...

Hardware

IBM appoints Gary D. Cohn as Vice Chairman ...

IBM appoints Martin Schroeter as CEO of "NewCo" independent managed infrastructure services business to spin out from IBM ...

Light-based processors boost machine-learning processing ...

E4 Computer Engineering announces University of Pisa as the first customer of Ultrafast Storage, Totally Integrated (USTI), the new solution for high performance distributed block storage ...

Existing Northern Data bitcoin mining customer expands contract volume by more than 200 MW ...

Power XL Pro launched as new professional server based on AMD EPYC technology ...

Swinburne-led research team demonstrates world's fastest optical neuromorphic processor ...

Applications

Supercomputer models describe chloride's role in corrosion ...

HPC-AI Advisory Council to host HPC AI AC Conference in Japan on January, 26 ...

Insights through atomic simulation ...

Advanced materials in a snap ...

New data-driven global climate model provides projections for urban environments ...

Frequency data for stable power supply ...

Physicists observe competition between magnetic orders ...

UTSA Artificial Intelligence Consortium receives over $1 million in research funding ...

Entangling electrons with heat ...

NIO partners with NVIDIA to develop a new generation of automated driving electric vehicles ...

Navantia leverages Ansys' digital transformation solutions to design next-gen naval vessels ...

Engineering graduate student places second in international research competition ...

The Cloud

Covid-19 genome sequencing project gets major upgrade ...

IBM provides Harris-Stowe State University with $2 million in Artificial Intelligence and open hybrid Cloud technology resources to help students build modern skills ...

IBM and Avertra collaborate to drive digital transformation for energy & utilities clients with IBM Cloud ...

UJET CCaaS Cloud contact centre now available on Oracle Cloud Marketplace ...