/PRCWT/Guangzhou, April 24, 2023——Hydrogen, the most abundant element in the universe, is found everywhere from the dust filling most of outer space to the cores of stars to many substances here on Earth. This would be reason enough to study hydrogen, but its individual atoms are also the simplest of any element with just one proton and one electron. For David Ceperley, a professor of physics at the University of Illinois Urbana-Champaign, this makes hydrogen the natural starting point for formulating and testing theories of matter.氢是宇宙中含量最丰富的元素，从 充满大部分外太空的尘埃到恒星的核心到许多物质 在地球上这将是研究氢的足够理由，但它的个体 原子也是最简单的元素，只有一个质子和一个 电子对于大卫·塞珀利来说，他是牛津大学的物理学教授 伊利诺伊州Urbana-Champaign，这使得氢成为天然的起点。 阐述和检验物质理论。
Ceperley, also a member of the Illinois Quantum Information Science and Technology Center, uses computer simulations to study how hydrogen atoms interact and combine to form different phases of matter like solids, liquids, and gases. However, a true understanding of these phenomena requires quantum mechanics, and quantum mechanical simulations are costly. To simplify the task, Ceperley and his collaborators developed a machine learning technique that allows quantum mechanical simulations to be performed with an unprecedented number of atoms. They reported in Physical Review Letters that their method found a new kind of high-pressure solid hydrogen that past theory and experiments missed.Ceperley，也是伊利诺伊州量子信息科学和 技术中心，使用计算机模拟来研究氢原子如何 相互作用并联合收割机以形成物质的不同相，如固体、液体 和气体。然而，真正理解这些现象需要量子 力学和量子力学模拟是昂贵的。为了简化任务， Ceperley及其合作者开发了一种机器学习技术 允许以前所未有的方式进行量子力学模拟 原子数。他们在《物理评论快报》上报道说他们的方法 发现了一种新的高压固体氢，过去的理论和 错过的实验
"Machine learning turned out to teach us a great deal," Ceperley said. "We had been seeing signs of new behavior in our previous simulations, but we didn't trust them because we could only accommodate small numbers of atoms. With our machine learning model, we could take full advantage of the most accurate methods and see what's really going on."“事实证明，机器学习教会了我们很多东西，”Ceperley说。“我们 在我们之前的模拟中，我一直看到新行为的迹象，但我们没有 因为我们只能容纳少量的原子。与我们的 机器学习模型，我们可以充分利用最准确的 看看到底发生了什么。”
Hydrogen atoms form a quantum mechanical system, but capturing their full quantum behavior is very difficult even on computers. A state-of-the-art technique like quantum Monte Carlo (QMC) can feasibly simulate hundreds of atoms, while understanding large-scale phase behaviors requires simulating thousands of atoms over long periods of time.氢原子形成了一个量子力学系统，但捕获它们的全部 量子行为即使在计算机上也是非常困难的。最先进的 像量子蒙特卡罗（QMC）这样技术可以可行地模拟数百个 原子，而了解大规模的相行为需要模拟 数千个原子在很长一段时间内。
To make QMC more versatile, two former graduate students, Hongwei Niu and Yubo Yang, developed a machine learning model trained with QMC simulations capable of accommodating many more atoms than QMC by itself. They then used the model with postdoctoral research associate Scott Jensen to study how the solid phase of hydrogen that forms at very high pressures melts.为了使QMC更加多才多艺，两位前研究生，Hongwei Niu和 Yubo Yang开发了一个用QMC模拟训练的机器学习模型 能够容纳比QMC本身更多的原子。然后他们使用了 模型与博士后研究助理斯科特詹森研究如何固体 在非常高的压力下形成的氢相熔化。
The three of them were surveying different temperatures and pressures to form a complete picture when they noticed something unusual in the solid phase. While the molecules in solid hydrogen are normally close-to-spherical and form a configuration called hexagonal close packed—Ceperley compared it to stacked oranges—the researchers observed a phase where the molecules become oblong figures—Ceperley described them as egg-like.他们三个在测量不同的温度和压力 当他们注意到固相中的一些不寻常的东西时，他们就得到了一张完整的照片。而 固体氢中的分子通常接近球形，并形成 一种称为六边形紧密堆积的结构-Ceperley将其与堆叠结构进行了比较 橙色-研究人员观察到分子变成椭圆形的阶段 Figures-Ceperley将它们描述为鸡蛋状。
"We started with the not-too-ambitious goal of refining the theory of something we know about," Jensen recalled. "Unfortunately, or perhaps fortunately, it was more interesting than that. There was this new behavior showing up. In fact, it was the dominant behavior at high temperatures and pressures, something there was no hint of in older theory."“我们从一个不太雄心勃勃的目标开始，即完善 我们知道的东西，“詹森回忆说。“不幸的是，或者也许 幸运的是，它比那更有趣。有一种新的行为 出现事实上，这是在高温和 压力，这是旧理论中没有的暗示。”
To verify their results, the researchers trained their machine learning model with data from density functional theory, a widely used technique that is less accurate than QMC but can accommodate many more atoms. They found that the simplified machine learning model perfectly reproduced the results of standard theory. The researchers concluded that their large-scale, machine learning-assisted QMC simulations can account for effects and make predictions that standard techniques cannot.为了验证他们的结果，研究人员训练了他们的机器学习模型 密度泛函理论的数据，这是一种广泛使用的技术， 比QMC精确，但可以容纳更多的原子。他们发现 简化的机器学习模型完美地再现了标准的结果 理论研究人员得出结论，他们的大规模，机器 学习辅助的QMC模拟可以解释影响并做出预测 这是标准技术无法做到的。
This work has started a conversation between Ceperley's collaborators and some experimentalists. High-pressure measurements of hydrogen are difficult to perform, so experimental results are limited. The new prediction has inspired some groups to revisit the problem and more carefully explore hydrogen's behavior under extreme conditions.这项工作已经开始了Ceperley的合作者和 一些实验者。氢的高压测量很难 因此，实验结果有限。新的预测激发了 一些小组重新审视这个问题，更仔细地探索氢的 极端条件下的行为。
Ceperley noted that understanding hydrogen under high temperatures and pressures will enhance our understanding of Jupiter and Saturn, gaseous planets primarily made of hydrogen. Jensen added that hydrogen's "simplicity" makes the substance important to study. "We want to understand everything, so we should start with systems that we can attack," he said. "Hydrogen is simple, so it's worth knowing that we can deal with it."Ceperley指出，了解高温下的氢和 压力将增强我们对木星和土星这两颗气态行星的了解 主要由氢组成。詹森补充说，氢的“简单性”使得 重要的研究内容。“我们想了解一切，所以我们应该 从我们可以攻击的系统开始，”他说。“氢很简单，所以它是 值得知道我们能处理好”
