首页|Lawrence Livermore National Laboratory Reports Findings in Machine Learning (Con finement Effects on Proton Transfer in TiO2 Nanopores from Machine Learning Pote ntial Molecular Dynamics Simulations)
Lawrence Livermore National Laboratory Reports Findings in Machine Learning (Con finement Effects on Proton Transfer in TiO2 Nanopores from Machine Learning Pote ntial Molecular Dynamics Simulations)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning is the subject o f a report. According to news reporting out of Livermore, California, by NewsRx editors, research stated, "Improved understanding of proton transfer in nanopore s is critical for a wide range of emerging applications, yet experimentally prob ing mechanisms and energetics of this process remains a significant challenge. T o help reveal details of this process, we developed and applied a machine learni ng potential derived from first-principles calculations to examine water reactiv ity and proton transfer in TiO slit-pores." Our news journalists obtained a quote from the research from Lawrence Livermore National Laboratory, "We find that confinement of water within pores smaller tha n 0.5 nm imposes strong and complex effects on water reactivity and proton trans fer. Although the proton transfer mechanism is similar to that at a TiO interfac e with bulk water, confinement reduces the activation energy of this process, le ading to more frequent proton transfer events. This enhanced proton transfer ste ms from the contraction of oxygen-oxygen distances dictated by the interplay bet ween confinement and hydrophilic interactions. Our simulations also highlight th e importance of the surface topology, where faster proton transport is found in the direction where a unique arrangement of surface oxygens enables the formatio n of an ordered water chain."
LivermoreCaliforniaUnited StatesNo rth and Central AmericaCyborgsEmerging TechnologiesMachine LearningMolec ular DynamicsPhysics
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