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Research Highlight | Center for Nanoscale Materials

Reinforcement learning algorithm captures previously inaccessible mesoscale phenomena

In a Journal of Chemical Physics paper, scientists present a reinforcement learning algorithm that effectively models mesoscale aggregation phenomena in liquid-liquid mixtures.

Scientific Achievement

An optimization algorithm based on reinforcement learning overcomes the limitations of brute-force all-atom simulations applying molecular dynamics. It yields 20 times more computationally efficient exploration of mesoscale physics, such as that of liquid-liquid extraction of metal ions.

Significance and Impact

This reinforcement learning-based algorithm captures phase separation (boiling, crystallization, etc.) while describing the many interactions present in complex solutions. It would benefit mesoscale study of liquid-liquid extraction, which is commonly used to separate metal ions from solutions.

Research Details

  • For comparison with model calculations, small-angle X-ray scattering patterns were obtained for extractant and solvents of varied chain lengths—octane, dodecane, and hexadecane.

DOI: 10.1063/5.0151050

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The Center for Nanoscale Materials is one of the five DOE Nanoscale Science Research Centers, premier national user facilities for interdisciplinary research at the nanoscale supported by the DOE Office of Science. Together the NSRCs comprise a suite of complementary facilities that provide researchers with state-of-the-art capabilities to fabricate, process, characterize and model nanoscale materials, and constitute the largest infrastructure investment of the National Nanotechnology Initiative. The NSRCs are located at DOE’s Argonne, Brookhaven, Lawrence Berkeley, Oak Ridge, Sandia and Los Alamos National Laboratories. For more information about the DOE NSRCs, please visit https://​sci​ence​.osti​.gov/​U​s​e​r​-​F​a​c​i​l​i​t​i​e​s​/​U​s​e​r​-​F​a​c​i​l​i​t​i​e​s​-​a​t​-​a​-​G​lance.

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