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Primeur weekly 2017-02-20

Focus

HPC expert Genias Benelux to show its skillful expertise in brandnew website ...

Are billion Euro Flagships the right way to finance innovative areas like graphene, human brain research and quantum computing? ...

Exascale supercomputing

Advanced fusion code led by PPPL selected to participate in Early Science Programmes on three new DOE Office of Science pre-exascale supercomputers ...

Focus on Europe

From robotics to particle physics: Data analytics gets the spotlight in Distinguished Talk series at ISC 2017 ...

A new spin on electronics ...

Data mining tools for personalized cancer treatment ...

Why host HPC in Iceland to tackle Big Data for life sciences at Earlham Insititute ...

Biological experiments become transparent - anywhere, any time ...

Middleware

IBM delivers new platform to help clients address storage challenges at massive scale ...

Hewlett Packard Enterprise unveils most significant 3PAR Flash storage innovations to date ...

Hardware

Tokyo Institute of Technology partners with DDN on Tsubame3.0 to build forward-looking AI and Big Data computing infrastructure ...

Mellanox demonstrates four times improvement in crypto performance with Innova IPsec 40G Ethernet network adapter ...

Supermicro launches BigTwin - the industry's highest performing Twin multi-node system supporting the full range of CPUs, maximum memory and all-flash NVMe ...

Applications

Researchers catch extreme waves with higher-resolution modelling ...

Researchers are creating software to 'clean' large datasets, making it easier for scientists and the public to use Big Data ...

Designing new materials from 'small' data ...

Success by deception ...

DNA computer brings 'intelligent drugs' a step closer ...

'Lossless' metamaterial could boost efficiency of lasers and other light-based devices ...

Perimeter Institute researchers apply machine learning to condensed matter physics ...

When treating brain aneurysms, two isn't always better than one ...

Real-time MRI analysis powered by supercomputers ...

Analyzing data for transportation systems using TACC's Rustler, XSEDE ECSS support ...

NCSA facilitates performance comparisons with China's nr. 1 supercomputer ...

IBM delivers Watson for cyber security to power cognitive security operations centres ...

The Cloud

Optimizing data centre placement and network design to strengthen Cloud computing ...

Dutch start-up solution impacts data centres ...

OpenFog Consortium releases landmark reference architecture for Fog computing ...

IBM brings machine learning to the private Cloud ...

IBM accelerates hybrid Cloud adoption by enabling channel partners to offer VMware solutions ...

Oracle launches Cloud service to help organisations integrate disparate data and drive real-time analytics ...

Designing new materials from 'small' data

This is the novel data science approach using machine learning to find promising materials from small data. Credit: James Rondinelli.17 Feb 2017 Evanston - Finding new functional materials is always tricky. But searching for very specific properties among a relatively small family of known materials is even more difficult.

But a team from Northwestern Engineering and Los Alamos National Laboratory found a workaround. The group developed a novel workflow combining machine learning and density functional theory calculations to create design guidelines for new materials that exhibit useful electronic properties, such as ferro-electricity and piezo-electricity.

Few layered materials have these qualities in certain geometries - crucial for developing solutions to electronics, communication, and energy problems - meaning there was very little data from which to formulate the guidelines using traditional research approaches.

"When others look for new materials, typically they look in places where they have a lot of data from similar materials. It's not necessarily easy by any means, but we do know how to distill information from large datasets", stated James M. Rondinelli, assistant professor of materials science and engineering in the McCormick School of Engineering. "When you don't have a lot of information, learning from the data becomes a difficult problem."

The research is described in the paper " Learning from data to design functional materials without inversion symmetry ", appearing in the February 17, 2017, issue ofNature Communications. Prasanna Balachandran of Los Alamos National Lab in New Mexico is the paper's co-author. Joshua Young, a former graduate student in James M. Rondinelli's lab, and Turab Lookman, a senior researcher at Los Alamos, also contributed.

Supported by funding from the National Science Foundation and the Laboratory Directed Research and Development Programme through Los Alamos, James M. Rondinelli's group focused on a class of two-dimensional complex oxides - or Ruddlesden-Popper oxides. These materials exhibit many technology-enabling properties, such as ferro-electricity and piezo-electricity, and can be interfaced with traditional semiconductor materials found in today's electronic devices.

"In this family, the data set is puny. Currently, there are only around 10 to 15 materials that are known with the desired properties", James M. Rondinelli stated. "This is not much data to work with. Traditionally data science is used for big data problems where there is less of a need for domain knowledge."

"Despite the small data nature of the problem", Prasanna Balachandran added: "Our approach worked because we were able to combine our understanding of these materials (domain knowledge) with the data to inform the machine learning."

Therefore, the group began building a database of known materials and using machine learning, a subfield of computer science that builds algorithms capable of learning from data and then using that learning to make better predictions. "With machine learning, we are able to identify chemical compositions that are likely candidates for the material you want to develop", he stated.

Of the more than 3000 possible materials investigated, the data science approach found more than 200 with promising candidates. Next, the team applied several types of rigorous quantum mechanical calculations. This assessed the atomic structures of the potential materials and checked their stability.

"We wondered: Would the material have the predicted structure? Does it have electric polarization? Can it be made in a laboratory?" James M. Rondinelli added.

This work narrowed the possibilities to 19, which were recommended for immediate experimental synthesis. Yet there are likely many more possibilities among the 200 candidates.

Typically, when developing new materials, the number of possibilities is too large to explore and develop each one. The process of screening potential materials is very expensive, and scientists must be selective in their investments.

"Our work has the potential to help save enormous amounts of time and resources", Prasanna Balachandran stated. "Instead of exploring all possible materials, only those materials that have the potential to be promising will be recommended for experimental investigation."

Source: Northwestern University

Back to Table of contents

Primeur weekly 2017-02-20

Focus

HPC expert Genias Benelux to show its skillful expertise in brandnew website ...

Are billion Euro Flagships the right way to finance innovative areas like graphene, human brain research and quantum computing? ...

Exascale supercomputing

Advanced fusion code led by PPPL selected to participate in Early Science Programmes on three new DOE Office of Science pre-exascale supercomputers ...

Focus on Europe

From robotics to particle physics: Data analytics gets the spotlight in Distinguished Talk series at ISC 2017 ...

A new spin on electronics ...

Data mining tools for personalized cancer treatment ...

Why host HPC in Iceland to tackle Big Data for life sciences at Earlham Insititute ...

Biological experiments become transparent - anywhere, any time ...

Middleware

IBM delivers new platform to help clients address storage challenges at massive scale ...

Hewlett Packard Enterprise unveils most significant 3PAR Flash storage innovations to date ...

Hardware

Tokyo Institute of Technology partners with DDN on Tsubame3.0 to build forward-looking AI and Big Data computing infrastructure ...

Mellanox demonstrates four times improvement in crypto performance with Innova IPsec 40G Ethernet network adapter ...

Supermicro launches BigTwin - the industry's highest performing Twin multi-node system supporting the full range of CPUs, maximum memory and all-flash NVMe ...

Applications

Researchers catch extreme waves with higher-resolution modelling ...

Researchers are creating software to 'clean' large datasets, making it easier for scientists and the public to use Big Data ...

Designing new materials from 'small' data ...

Success by deception ...

DNA computer brings 'intelligent drugs' a step closer ...

'Lossless' metamaterial could boost efficiency of lasers and other light-based devices ...

Perimeter Institute researchers apply machine learning to condensed matter physics ...

When treating brain aneurysms, two isn't always better than one ...

Real-time MRI analysis powered by supercomputers ...

Analyzing data for transportation systems using TACC's Rustler, XSEDE ECSS support ...

NCSA facilitates performance comparisons with China's nr. 1 supercomputer ...

IBM delivers Watson for cyber security to power cognitive security operations centres ...

The Cloud

Optimizing data centre placement and network design to strengthen Cloud computing ...

Dutch start-up solution impacts data centres ...

OpenFog Consortium releases landmark reference architecture for Fog computing ...

IBM brings machine learning to the private Cloud ...

IBM accelerates hybrid Cloud adoption by enabling channel partners to offer VMware solutions ...

Oracle launches Cloud service to help organisations integrate disparate data and drive real-time analytics ...