Rapid Prediction of Bimetallic Mixing Behavior at the Nanoscale
Rapid Prediction of Bimetallic Mixing Behavior at the Nanoscale
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DOI：
10.1021/acsnano.0c01586
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发表时间：
2020-07-28
期刊：
影响因子：
17.1
通讯作者：
Mpourmpakis, Giannis
中科院分区：
文献类型：
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作者：
Dean, James;Cowan, Michael J.;Mpourmpakis, Giannis
纳米颗粒（NP）设计空间允许尺寸、形状、组成和化学顺序的变化。在搜索低能量结构时，这会导致搜索空间非常大，无法通过蛮力方法进行筛选。在这项工作中，我们开发了一种遗传算法来预测任何大小，形状和金属成分的稳定的COGNPs。我们的方法预测纳米结构与实验趋势一致，它捕获了实验的23，196个原子的FePt NP的详细化学排序，几乎是原子对原子的准确性。我们开发的筛选过程非常快，使我们能够生成和分析5454个低能量CONONP的数据库。通过确定纳米级稳定的纳米颗粒，我们合理化的纳米混合，并揭示金属，尺寸和温度依赖的混合行为。重要的是，我们的方法适用于任何纳米级NP尺寸，在纳米级模拟中弥合材料差距，并通过阐明纳米级NP的稳定性，混合和详细的化学有序行为来指导实验室实验。
The nanoparticle (NP) design space allows for variations in size, shape, composition, and chemical ordering. In the search for low-energy structures, this results in an extremely large search space which cannot be screened by brute force methods. In this work, we develop a genetic algorithm to predict stable bimetallic NPs of any size, shape, and metal composition. Our method predicts nanostructures in agreement with experimental trends and it captures the detailed chemical ordering of an experimental 23,196-atom FePt NP with nearly atom-by-atom accuracy. Our developed screening process is extremely fast, allowing us to generate and analyze a database of 5454 low-energy bimetallic NPs. By identifying thermodynamically stable NPs, we rationalize bimetallic mixing at the nanoscale and reveal metal-, size-, and temperature-dependent mixing behavior. Importantly, our method is applicable to any bimetallic NP size, bridging the materials gap in nanoscale simulations, and guides experimentation in the lab by elucidating stability, mixing, and detailed chemical ordering behavior of bimetallic NPs.