首页|采用机器学习算法加速过渡金属碳/氮化物的开发

采用机器学习算法加速过渡金属碳/氮化物的开发

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目的 设计748种候选金属碳/氮化物(MAX)材料,预测其可合成性及热力学稳定性.方法 利用开放量子材料数据库(OQMD)中获得的数据集,采用一种基于机器学习方法的深度神经网络(DNN)模型,预测候选MAX材料的相对形成能并探究其与材料化学性质之间的相关性.结果 12种关于材料组成与结构的特征描述符解释了相对形成能和稳定性之间的定量关系,在所设计的748种MAX候选物中有339个具有较高的合成概率.与氮化物MAX候选物相比,碳化物MAX材料成功合成概率更高.结论 该项工作不仅发现了可能合成的MAX化合物,而且为小数据集提供了一种准确有效的机器学习方法,以揭示MAX相的物理与化学描述符和热力学稳定性之间的关系.
Enhanced discovery of transition-metal carbides and nitrides via machine learning
Purposes—To predict the composability and thermodynamic stability of 748 candidate metal carbon/nitride(MAX)materials which are designed in advance.Methods—With the dataset ob-tained from the open quantum materials database(OQMD),a deep neural network(DNN)model based on a machine learning method is used to predict the relative formation energies of candidate MAX materials and explore the correlation between such materials and their chemical properties.Re-sults—The quantitative relationship between relative formation energy and stability can be elucidated by several compositional and structural descriptors.The synthesis of 339 out of the total 748 MAX candidates is highly probable.In comparison to nitride MAX candidates,carbide MAX materials ex-hibit a greater probability of successful synthesis.Conclusions—This work not only discovers several promisingly stable MAX compounds but also develops an accurate and efficient ML on small data sets to reveal the relations between physical and chemical descriptors and thermodynamic stability of MAX phases.

MAXstabilitymachine learningrelative formation energy

康城、孙文卓、李亚欣、卫粉艳、黄卓楠

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宝鸡文理学院化学化工学院,陕西宝鸡 721013

MAX 稳定性 机器学习 相对形成能

陕西省科技厅青年项目

2021JQ-803

2024

宝鸡文理学院学报(自然科学版)
宝鸡文理学院

宝鸡文理学院学报(自然科学版)

影响因子:0.356
ISSN:1007-1261
年,卷(期):2024.44(1)
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