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The Economic Case for Wind and Solar Energy in Africa

To meet skyrocketing demand for electricity, African countries may have to triple their energy output by 2030. While hydropower and fossil fuel power plants are favored approaches in some quarters, a new assessment by Lawrence Berkeley National Laboratory has found that wind and solar can be economically and environmentally competitive options and can contribute significantly to the rising demand.

Chemists ID Catalytic 'Key' for Converting CO2 to Methanol

Results from experiments and computational modeling studies that definitively identify the "active site" of a catalyst commonly used for making methanol from CO2 will guide the design of improved catalysts for transforming this pollutant to useful chemicals.

Cryo-Electron Microscopy Achieves Unprecedented Resolution Using New Computational Methods

Cryo-electron microscopy (cryo-EM)--which enables the visualization of viruses, proteins, and other biological structures at the molecular level--is a critical tool used to advance biochemical knowledge. Now Berkeley Lab researchers have extended cryo-EM's impact further by developing a new computational algorithm instrumental in constructing a 3-D atomic-scale model of bacteriophage P22 for the first time.

New Study Maps Space Dust in 3-D

A new Berkeley Lab-led study provides detailed 3-D views of space dust in the Milky Way, which could help us understand the properties of this dust and how it affects views of distant objects.

Single-Angle Ptychography Allows 3D Imaging of Stressed Materials

Scientists have used a new X-ray diffraction technique called Bragg single-angle ptychography to get a clear picture of how planes of atoms shift and squeeze under stress.

New Feedback System Could Allow Greater Control Over Fusion Plasma

A physicist has created a new system that will let scientists control the energy and rotation of plasma in real time in a doughnut-shaped machine known as a tokamak.

Towards Super-Efficient, Ultra-Thin Silicon Solar Cells

Researchers from Ames Laboratory used supercomputers at NERSC to evaluate a novel approach for creating more energy-efficient ultra-thin crystalline silicon solar cells by optimizing nanophotonic light trapping.

Study IDs Link Between Sugar Signaling and Regulation of Oil Production in Plants

UPTON, NY--Even plants have to live on an energy budget. While they're known for converting solar energy into chemical energy in the form of sugars, plants have sophisticated biochemical mechanisms for regulating how they spend that energy. Making oils costs a lot. By exploring the details of this delicate energy balance, a group of scientists from the U.

High-Energy Electrons Probe Ultrafast Atomic Motion

A new technique synchronized high-energy electrons with an ultrafast laser pulse to probe how vibrational states of atoms change in time.

Rare Earth Recycling

A new energy-efficient separation of rare earth elements could provide a new domestic source of critical materials.


Valerie Taylor Named Argonne National Laboratory's Mathematics and Computer Science Division Director

Computer scientist Valerie Taylor has been appointed as the next director of the Mathematics and Computer Science division at Argonne, effective July 3, 2017.

Three SLAC Employees Awarded Lab's Highest Honor

At a March 7 ceremony, three employees of the Department of Energy's SLAC National Accelerator Laboratory were awarded the lab's highest honor ­- the SLAC Director's Award.

Dan Sinars Represents Sandia in First Energy Leadership Class

Dan Sinars, a senior manager in Sandia National Laboratories' pulsed power center, which built and operates the Z facility, is the sole representative from a nuclear weapons lab in a new Department of Energy leadership program that recently visited Sandia.

ORNL, HTS International Corporation to Collaborate on Manufacturing Research

HTS International Corporation and the Department of Energy's Oak Ridge National Laboratory have signed an agreement to explore potential collaborations in advanced manufacturing research.

Jefferson Lab Director Honored with Energy Secretary Award

Hugh Montgomery, director of the Department of Energy's Thomas Jefferson National Accelerator Facility (Jefferson Lab), was awarded The Secretary's Distinguished Service Award by the Secretary of Energy earlier this year.

New Projects to Make Geothermal Energy More Economically Attractive

Geothermal energy, a clean, renewable source of energy produced by the heat of the earth, provides about 6 percent of California's total power. That number could be much higher if associated costs were lower. Now scientists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have launched two California Energy Commission-funded projects aimed at making geothermal energy more cost-effective to deploy and operate.

Southern Research Project Advances Novel CO2 Utilization Strategy

The U.S. Department of Energy's Office of Fossil Energy has awarded Southern Research nearly $800,000 for a project that targets a more cost-efficient and environmentally friendly method of producing some of the most important chemicals used in manufacturing.

Harker School Wins 2017 SLAC Regional Science Bowl Competition

After losing its first match of the day to the defending champions, The Harker School's team won 10 consecutive rounds to claim victory in the annual SLAC Regional DOE Science Bowl on Saturday, Feb. 11.

Francis Alexander Named Deputy Director of Brookhaven Lab's Computational Science Initiative

Alexander brings extensive management and leadership experience in computational science research to the position.

Kalinin, Paranthaman Elected Materials Research Society Fellows

Two researchers at Oak Ridge National Laboratory, Sergei Kalinin and Mariappan Parans Paranthaman, have been elected fellows of the Materials Research Society.


High-Energy Electrons Probe Ultrafast Atomic Motion

A new technique synchronized high-energy electrons with an ultrafast laser pulse to probe how vibrational states of atoms change in time.

Rare Earth Recycling

A new energy-efficient separation of rare earth elements could provide a new domestic source of critical materials.

Modeling the "Flicker" of Gluons in Subatomic Smashups

A new model identifies a high degree of fluctuations in the glue-like particles that bind quarks within protons as essential to explaining proton structure.

Rare Nickel Atom Has "Doubly Magic" Structure

Supercomputing calculations confirm that rare nickel-78 has unusual structure, offering insights into supernovas.

Microbial Activity in the Subsurface Contributes to Greenhouse Gas Fluxes

Natural carbon dioxide production from deep subsurface soils contributes significantly to emissions, even in a semiarid floodplain.

Stretching a Metal Into an Insulator

Straining a thin film controllably allows tuning of the materials' magnetic, electronic, and catalytic properties, essential for new energy and electronic devices.

How Moisture Affects the Way Soil Microbes Breathe

Study models soil-pore features that hold or release carbon dioxide.

ARM Data Is for the Birds

Scientists use LIDAR and radar data to study bird migration patterns, thanks to the Atmospheric Radiation Measurement (ARM) Climate Research Facility.

The Future of Coastal Flooding

Better storm surge prediction capabilities could help reduce the impacts of extreme weather events, such as hurricanes.

Estimating Global Energy Use for Water-Related Processes

Scientists find that water-related energy consumption is increasing across the globe, with pronounced differences across regions and sectors.


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Great Neck South High School Wins Regional Science Bowl at Brookhaven Lab

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Haslam Visits ORNL to Highlight State's Role in Discovering Tennessine

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Internship Program Helps Foster Development of Future Nuclear Scientists

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More Than 12,000 Explore Jefferson Lab During April 30 Open House

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NMSU Undergrad Tackles 3D Particle Scattering Animations After Receiving JSA Research Assistantship

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Shannon Greco: A Self-Described "STEM Education Zealot"

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Meet Robert Palomino: 'Give Everything a Shot!'

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University of Utah Makes Solar Accessible

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Student Innovator at Rensselaer Polytechnic Institute Seeks Brighter, Smarter, and More Efficient LEDs

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Machine Learning Method Accurately Predicts Metallic Defects

Article ID: 668895

Released: 2017-02-03 17:05:33

Source Newsroom: Lawrence Berkeley National Laboratory

  • Credit: Bharat Medasan, Berkeley Lab/ PNNL

    Dominant defect type predictions from r-MART model for 946 B2-type intermetallics. Colors indicates the relationship between prediction and calculations as shown in the legend.

For the first time, researchers at the Lawrence Berkeley National Laboratory (Berkeley Lab) have built and trained machine learning algorithms to predict defect behavior in certain intermetallic compounds with high accuracy. This method will accelerate research of new advanced alloys and lightweight new materials for applications spanning automotive to aerospace and much more.

Their results were published in the December 2016 issue of Nature Computational Materials.

Materials are never chemically pure and structurally flawless. They almost always contain defects, which play an important role in dictating properties. These defects may appear as vacancies, which are essentially ‘holes’ in the substance’s crystal structure, or antisite defects, which are essentially atoms placed on the wrong crystal site. Understanding of such point defects is crucial for scientists designing materials because they can have a dramatic effect on long-time structural stability and strength.

Traditionally, researchers have used a computational quantum mechanical method known as density functional calculations to predict what kinds of defects can be formed in a given structure and how they affect the material’s properties. Although effective, this approach is very computationally expensive to execute for point defects limiting the scope of such investigations.

“Density functional calculations work well if you are modeling one small unit, but if you want to make your modeling cell bigger the computational power required to do this increases substantially,” says Bharat Medasani, a former Berkeley Lab postdoc and lead author of the npj paper. “And because it is computationally expensive to model defects in a single material, doing this kind of brute force modeling for tens of thousands of materials is not feasible.”

To overcome these computing challenges, Medasani and his colleagues developed and trained machine learning algorithms to predict point defects in intermetallic compounds, focusing on the widely observed B2 crystal structure. Initially, they selected a sample of 100 of these compounds from the Materials Project Database and ran density functional calculations on supercomputers at the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility at Berkeley Lab, to identify their defects.

Because they had a small data sample to work from, Medasani and his team used a forest approach called gradient boosting to develop their machine learning method to a high accuracy. In this approach additional machine learning models were built successively and combined with prior models to minimize the difference between the models predictions and the results from density functional calculations. The researchers repeated the process until they achieved a high level of accuracy in their predictions.

“This work is essentially a proof of concept. It shows that we can run density functional calculations for a few hundred materials, then train machine learning algorithms to accurately predict point defects for a much larger group of materials,” says Medasani, who is now a postdoctoral researcher at the Pacific Northwest National Laboratory.

“The benefit of this work is now we have a computationally inexpensive machine learning approach that can quickly and accurately predict point defects in new intermetallic materials ” says Andrew Canning, a Berkeley Lab Computational Scientist and co-author on the npj paper. “We no longer have to run very costly first principle calculations to identify defect properties for every new metallic compound.”

“This tool enables us to predict metallic defects faster and robustly, which will in turn accelerate materials design,” says Kristin Persson, a Berkeley Lab Scientist and Director of the Materials Project, an initiative aimed at drastically reducing the time needed to invent new materials by providing open web-based access to computed information on known and predicted materials. As an extension of this work an open source Python toolkit for modeling point defects in semiconductors and insulators (PyCDT) has been developed.

In addition to Medasani, Canning and Persson, other authors on the NatureComputational Materials paper include: Hong Ding, Wei Chen, Mark Asta and Maciej Haranczyk (Berkeley Lab); and Anthony Gamst (University of California, San Diego). Additionally, Danny Broberg (University of California, Berkeley), Geoffroy Hautier (University Catholique de Louvain, Belgium) and Nils Zimmermann (Berkeley Lab) were involved in the development of the PyCDT software.

The research was supported by the Department of Energy’s Office of Science.