首页|Reports from Los Alamos National Laboratory Highlight Recent Findings in Machine Learning (Characterizing Different Motilityinduced Regimes In Active Matter With Machine Learning and Noise)
Reports from Los Alamos National Laboratory Highlight Recent Findings in Machine Learning (Characterizing Different Motilityinduced Regimes In Active Matter With Machine Learning and Noise)
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Investigators publish new report on Machine Learning. According to news originating from Los Alamos, New Mexico, by NewsRx correspondents, research stated, “We examine motility-induced phase separation (MIPS) in two-dimensional run-and-tumble disk systems using both machine learning and noise fluctuation analysis. Our measures suggest that within the MIPS state there are several distinct regimes as a function of density and run time, so that systems with MIPS transitions exhibit an active fluid, an active crystal, and a critical regime.” Financial supporters for this research include U.S. Department of Energy through the Los Alamos National Laboratory, United States Department of Energy (DOE), M. J. Murdock Charitable Trust. Our news journalists obtained a quote from the research from Los Alamos National Laboratory, “The different regimes can be detected by combining an order parameter extracted from principal component analysis with a cluster stability measurement. The principal component-derived order parameter is maximized in the critical regime, remains low in the active fluid, and has an intermediate value in the active crystal regime. We demonstrate that machine learning can better capture dynamical properties of the MIPS regimes compared to more standard structural measures such as the maximum cluster size. The different regimes can also be characterized via changes in the noise power of the fluctuations in the average speed.”
Los AlamosNew MexicoUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningLos Alamos National Laboratory
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