镁簇MgN (N≤100)全局优化的高斯逼近势参数化

IF 2.9 3区 化学 Q3 CHEMISTRY, PHYSICAL
Ilya Sergeevich Steshin, Sergey Panteleev, Igor Valerievich Petukhov, Stanislav K. Ignatov
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引用次数: 0

摘要

采用两阶段GridSearch和主动学习相结合的方法优化了Mg簇的GAP模型参数,在外推域Mgn, n >中实现了可靠的结构预测;50. 利用参数化GAP模型的全局优化结果显示,Mg51-Mg53团簇在能量上是有利的,显示出先前仅在Mg54中报道的金字塔形核心形成的早期开始。全局优化确定了“神奇”Mg59、Mg69、Mg74和Mg99星团的新的全局最小候选者。hcp样基序的存在对含有Mgn, n <的簇的结构稳定性没有显著影响;100,因为在转基因“魔法”集群和其他类似大小的集群之间没有观察到结构差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parametrization of Gaussian Approximation Potential for the Global Optimization of Magnesium Clusters MgN (N≤100)
A two-stage GridSearch combined with active learning was employed to optimize GAP model parameters for Mg clusters, enabling reliable structural predictions in the extrapolative domain Mgn, n > 50. Global optimization using the parameterized GAP model revealed energetically favorable Mg51-Mg53 clusters, showing early onset of pyramidal core formation previously reported only from Mg54. Global optimization identified new global minima candidates for the "magic" Mg59, Mg69, Mg74 and Mg99 clusters. The presence of hcp-like motifs doesn’t significantly influence structural stability in clusters with Mgn, n < 100, as no structural differences were observed between GM “magic” clusters and others of similar size.
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来源期刊
Physical Chemistry Chemical Physics
Physical Chemistry Chemical Physics 化学-物理:原子、分子和化学物理
CiteScore
5.50
自引率
9.10%
发文量
2675
审稿时长
2.0 months
期刊介绍: Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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