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Theory, Modeling and Simulation

ORNL conducts a broad range of theoretical research in the physical sciences with over 60 staff members and additional students, post-doctoral associates and visitors. This work is tightly integrated with experimental programs and is committed to making effective use of modern theory and advanced computation to progress core science and technology. Efforts include a full range of theory activities, ranging from basic science aimed at providing the fundamental basis for long-term solutions to our energy problems, to near-term work addressing our nation's most pressing energy and security needs. Work is highlighted by:

  • Cross-cutting capabilities/efforts impacting multiple ORNL programs and activities centered on nanoscience, physics, chemistry, materials, and neutron science
  • New theory and computational approaches to establish and enhance links with experiments
  • First principles methods based on density functional theory, quantum chemistry, classical and ab initio molecular dynamics, transport theory, many-body theory, quantum Monte Carlo, field theoretic approaches, phase field analysis, and statistical mechanics
  • Guiding understanding and providing prediction of new materials, architectures and reactions before they are realized in the experimental labs
  • Illuminating connections between experimental observations across diverse characterization techniques
  • Identifying new synthetic pathways

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1-5 of 236 Results

Confining Liquids in Hollow Nanospheres Can Yield Superior Quasi-Solid Electrolytes
— The growth and proliferation of lithium dendrites during cell recharge seriously hinder development and application of rechargeable Li-metal batteries. Researchers developed a promising strategy for fabrication of quasi−solid electrolytes with superior lithium ionic conductivities, by using a hollow silica (HS) nanosphere-film architecture that blocks dendrites.

Materials scientists use ORNL’s CADES to transform big data to ‘smart data’ for rapid image analysis
— Materials scientists use ORNL’s CADES to transform big data to ‘smart data’ for rapid image analysis. ORNL material sciences researchers are collaborating with computer scientists in ORNL’s Compute and Data Environment for Science (CADES) to create a processing and analysis workflow for the expansive scanning probe and electron microscopy data generated at the Center for Nanophase Materials Sciences (CNMS).

Researchers use machine learning to find useful structural properties in neutron and x-ray data
— Using CADES compute and data resources, researchers are linking DOE experimental and computational facilities to uncover stacking faults in double-layered perovskite. Here is the title and blurb to use on the webpage: Researchers use machine learning to find useful structural properties in neutron and x-ray data. A team of ORNL researchers is using the lab’s Compute and Data Environment for Science (CADES) to analyze large volumes of neutron and x-ray scattering data to find and identify these defects—a first step to greatly reducing time researchers spend on comparing and contrasting scattering data to identify connections between structure and function.

True structure of pnictide 122 superconductors revealed
— High-resolution microscopy revealed an unexpected room-temperature crystal structure of the ‘122’ Ba(Fe1-xCox)2As2 superconductors, with domains similar to those in ferroelectrics but with nanometer size.

New model predicts formation of stable high-entropy alloys
— Researchers devised a model that can predict which combinations of 5 or more elements will form new “high-entropy alloys.” This work, which utilizes values obtained from data mining of high-throughput calculations of binary compounds, requires no experimental or empirically derived input and advances capabilities for “materials by design.


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