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-调查人员讨论机器学习的新发现。根据来自加利福尼亚州利弗莫尔的新闻，NEWSRX记者报道，研究表明：“精确测定功能材料中的原子信息具有变革潜力和对新兴技术的广泛意义。光谱技术，如X射线吸收近边缘结构(XANES)，已经被广泛应用于材料表征；然而，从实验探针中提取化学信息仍然是一个巨大的挑战，特别是对于无序材料。”本研究的资金支持者包括美国能源部(DOE)、美国能源部(DOE)、实验室指导研究与发展(LDRD)计划、美国能源部(DOE)。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news originating from Livermore, California, by N ewsRx correspondents, research stated, "Precise determination of atomic structur al information in functional materials holds transformative potential and broad implications for emerging technologies. Spectroscopic techniques, such as X-ray absorption near-edge structure (XANES), have been widely used for material chara cterization; however, extracting chemical information from experimental probes r emains a significant challenge, particularly for disordered materials." Financial supporters for this research include United States Department of Energ y (DOE), United States Department of Energy (DOE), Laboratory Directed Research and Development (LDRD) program, United States Department of Energy (DOE).
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