摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-调查人员发布了关于马学习的新报告。根据来自俄罗斯莫斯科的新闻，由NewsRx Cor应答者报道，研究表明：“描述了精确表征石墨和金刚石相中碳原子相互作用的机器学习电位LS(MLP)的参数化。训练集由各种同素异形体的碳及其化合物组成。”这项研究的财政支持来自俄罗斯科学基金会(RSF)。我们的新闻记者引用了美国科学技术大学MISiS的一篇研究文章:“MLP是用从头算模拟得到的FO、能量和应力张量训练的,它本身可以准确地再现碳相的弹性性质和结构参数。”由于训练集的限制和MLPs中缺乏长期互动，MLPs还预测了一些非物理行为。根据新闻编辑的说法，这项研究的结论是：“尽管有这些限制，MLPs仍然是一种很有前途的工具，可以准确地描述石墨中金刚石的氧化过程。”这项研究已经经过同行评审。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news originating from Moscow, Russia, by NewsRx cor respondents, research stated, "The parameterization of machine learning potentia ls (MLP) for precise characterization of the interaction between carbon atoms in graphite and diamond phases is described. The training set consisted of various allotropic forms of carbon and their compounds." Financial support for this research came from Russian Science Foundation (RSF). Our news journalists obtained a quote from the research from the National Univer sity of Science & Technology MISiS, "The MLPs are trained using fo rces, energies, and stress tensors obtained from ab initio simulations. It is sh own that the MLPs can accurately reproduce elastic properties and structural par ameters of carbon phases. However, the MLPs also predict some unphysical behavio r due to the training set limitations and the lack of long-range interactions in the MLPs." According to the news editors, the research concluded: "In spite of these limita tions, MLPs are a promising tool for the accurate characterization of diamond nu cleation in graphite." This research has been peer-reviewed.
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