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Primeur weekly 2017-10-09

Special

2017 - Another year on the Road to Exascale: The Post-Moore era ...

Focus

Outsourcing your data centre to Verne Global in Iceland reduces costs and benefits climate ...

Draft work programme for 2018 - 2020 of the European Horizon 2020 funding programme shows strong commitment to HPC and exascale with over 450 million euro funding available ...

Exascale supercomputing

IDEAS goes big: Fostering better software development for exascale ...

Assessing regional earthquake risk and hazards in the age of exascale ...

Quantum computing

Bristol scientists pinpoint the singularity for quantum computers ...

New 'building material' points toward quantum computers ...

Focus on Europe

Call now open for ISC 2018 research papers: Submission ends December 22, 2017 ...

Project TopWing published top innovative results in Nature ...

PRACE 15th Call continues to award outstanding research in HPC ...

PRACE met future scientists at EUCYS 2017 ...

Dutch National Big Data Infrastructure will boost pre-competitive research ...

Middleware

Bright Computing rolls out tiered partner programme for resellers in North and South America ...

Hardware

CENATE names new director ...

University of South Dakota gets HPC cluster for research computing ...

Mellanox enables the next generation of software-defined data centre networks with BlueField SmartNIC network adapters ...

Discovery and innovation to quicken with supercomputer's $1-million upgrade ...

Applications

Cell modelling tool makes complex calculations user-friendly ...

Purdue professor wins Department of Defense computing award ...

Columbia researchers observe exotic quantum particle in bilayer graphene ...

MareNostrum supercomputer has allocated 20 million hours of calculation to the Nobel Prize for Physics winning project ...

The world-record 53.3 Tb/s optical switching capacity for data-centre networks ...

Chemists teach computer programme to model forces between atoms accurately ...

The Cloud

New Oracle Exadata X7 delivers in-memory performance from shared storage ...

Oracle unveils world's first autonomous database Cloud ...

Chemists teach computer programme to model forces between atoms accurately

This is a slider. Credit: MIPT Press Office.3 Oct 2017 Moscow - A team of researchers from MIPT, Skoltech, and Dukhov Research Institute of Automatics, led by Artem Oganov, used a machine learning technique to model the behaviour of aluminum and uranium in the liquid and crystalline phases at various temperatures and pressures. Such simulations of chemical systems can predict their properties under a range of conditions before experiments are performed, enabling further work with only the most promising materials. The research findings were published in the journalScientific Reports.

Rapid advances in science over the last 100 years have resulted in the discovery of an astonishing number of organic and inorganic compounds, protein and lipid structures, and chemical reactions. But with all these new structures and molecules, an increasing amount of time is necessary to study their make-up, biochemical and physical properties, as well as test the models of their behaviour under various conditions and their possible interactions with other compounds. Such research can now be accelerated using computer modelling.

The force field approach is the currently dominant modeling technique. It makes use of a set of parameters describing a given biochemical system. These include bond lengths and angles, and charges, among others. However, this technique is unable to accurately reproduce the quantum mechanical forces at play in molecules. Accurate quantum mechanical calculations are time-consuming. Besides, they only enable predictions of the behaviour of samples that are at best several hundred atoms large.

Machine learning approaches to molecular modelling are of great interest to chemists. They enable models that are trained on relatively small data sets obtained by means of quantum mechanical calculations. Such models can then replace quantum mechanical calculations, because they are just as accurate and require about 1,000 times less computing power.

The researchers used machine learning to model the interactions between atoms in crystalline and liquid aluminum and uranium. Aluminum is a well-studied metal whose physical and chemical properties are known to scientists. Uranium, by contrast, was chosen because there are conflicting published data on its physical and chemical properties, which the researchers sought to define more accurately.

The paper details their study of such material properties as the phonon density of states, entropy, and the melting temperature of aluminum.

"The magnitudes of interatomic forces in crystals can be used to predict how atoms of the same element will behave under different temperatures and in a different phase", stated Ivan Kruglov from the Computational Materials Design Laboratory at MIPT. "By the same token, you can use the data on the properties of a liquid to find out how the atoms will behave in a crystal. This means that by finding out more about the crystal structure of uranium, we can eventually reconstruct the entire phase diagram for this metal. Phase diagrams are charts indicating the properties of elements as a function of pressure and temperature. They are used to determine the limits to the applicability of a given element."

To make sure that the data yielded by computer simulations is valid, they are compared to experimental results. The method used by the researchers was in good agreement with prior experiments. The information obtained with the approach based on machine learning had a lower error rate, compared to the modelling techniques using force fields.

In this study , the authors improve on their 2016 results in terms of the speed and accuracy of atomic system modelling using machine learning.

Source: Moscow Institute of Physics and Technology - MIPT

Back to Table of contents

Primeur weekly 2017-10-09

Special

2017 - Another year on the Road to Exascale: The Post-Moore era ...

Focus

Outsourcing your data centre to Verne Global in Iceland reduces costs and benefits climate ...

Draft work programme for 2018 - 2020 of the European Horizon 2020 funding programme shows strong commitment to HPC and exascale with over 450 million euro funding available ...

Exascale supercomputing

IDEAS goes big: Fostering better software development for exascale ...

Assessing regional earthquake risk and hazards in the age of exascale ...

Quantum computing

Bristol scientists pinpoint the singularity for quantum computers ...

New 'building material' points toward quantum computers ...

Focus on Europe

Call now open for ISC 2018 research papers: Submission ends December 22, 2017 ...

Project TopWing published top innovative results in Nature ...

PRACE 15th Call continues to award outstanding research in HPC ...

PRACE met future scientists at EUCYS 2017 ...

Dutch National Big Data Infrastructure will boost pre-competitive research ...

Middleware

Bright Computing rolls out tiered partner programme for resellers in North and South America ...

Hardware

CENATE names new director ...

University of South Dakota gets HPC cluster for research computing ...

Mellanox enables the next generation of software-defined data centre networks with BlueField SmartNIC network adapters ...

Discovery and innovation to quicken with supercomputer's $1-million upgrade ...

Applications

Cell modelling tool makes complex calculations user-friendly ...

Purdue professor wins Department of Defense computing award ...

Columbia researchers observe exotic quantum particle in bilayer graphene ...

MareNostrum supercomputer has allocated 20 million hours of calculation to the Nobel Prize for Physics winning project ...

The world-record 53.3 Tb/s optical switching capacity for data-centre networks ...

Chemists teach computer programme to model forces between atoms accurately ...

The Cloud

New Oracle Exadata X7 delivers in-memory performance from shared storage ...

Oracle unveils world's first autonomous database Cloud ...