摘要
由一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据NewsRx编辑在加州利弗莫尔的新闻报道，研究表明：“提高对纳米孔中质子转移的理解对于广泛的新兴应用是至关重要的，但实验验证这一过程的机理和能量学仍然是一个重大挑战。帮助揭示这一过程的细节，我们开发并应用了由第一性原理计算得到的机器学习势来研究TiO缝隙孔中水的反应性和质子转移。我们的新闻记者从劳伦斯利弗莫尔国家实验室的研究中得到一句话：“我们发现，在0.5nm以下的孔隙中限制水对水的反应性和质子转移有强烈而复杂的影响。尽管质子转移机制类似于在TiO界面上与散装水的质子转移机制，但限制降低了该过程的活化能。”介绍了更频繁的质子转移事件。这种增强质子转移是由于限制和亲水相互作用所决定的氧-氧距离的收缩。我们的模拟也强调了表面拓扑结构的重要性，在表面氧的独特排列使形成有序的水链的方向上发现更快的质子转移。
Abstract
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."
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