Evidence-based recommender system and experimental validation for high-entropy alloys
Evidence-based recommender system and experimental validation for high-entropy alloys
复制标题
高熵合金的循证推荐系统和实验验证
DOI：
10.21203/rs.3.rs-208862/v1
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
H. Dam
中科院分区：
文献类型：
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作者：
Minh;Nguyen Nguyen;Viet;T. Nagata;T. Chikyow;H. Kino;T. Miyake;T. Denœux;V. Huynh;H. Dam
我们提出一种数据驱动的方法来探索高熵合金(HEA)。为了克服元素组合候选者众多，选择合适的描述符，以及现有数据的局限性和偏差的挑战，我们应用证据理论开发了一个无描述符的基于证据的推荐系统(ERS)。建议的系统衡量元素组合之间的相似性，并利用它来推荐潜在的家用电器。为了评估ERS，我们比较了它的HEA推荐能力与基于矩阵分解和监督学习的推荐系统在四个广为人知的数据集上的推荐能力，包括二元和三元合金。在数据集上使用k重交叉验证的实验结果表明，ERS的性能优于所有竞争对手。此外，在推荐四元和五元HEA的实验中，ERS表现出了良好的外推能力。我们在实验上验证了最强烈推荐的Fe-Co基磁性HEA，即。FeCoMnNi，证实其为体心立方结构，在高温下稳定。
We present a data-driven approach to explore high-entropy alloys (HEAs). To overcome the challenges with numerous element-combination candidates, selecting appropriate descriptors, and the limitations and biased of existing data, we apply the evidence theory to develop a descriptor-free evidence-based recommender system (ERS) for recommending HEAs. The proposed system measures the similarities between element combinations and utilizes it to recommend potential HEAs. To evaluate the ERS, we compare its HEA-recommendation capability with those of matrix-factorization- and supervised-learning-based recommender systems on four widely known data sets, including binary and ternary alloys. The results of experiments using k-fold cross-validation on the data sets show that the ERS outperforms all competitors. Furthermore, the ERS shows excellent extrapolation capabilities in experiments of recommending quaternary and quinary HEAs. We experimentally validate the most strongly recommended Fe-Co-based magnetic HEA, viz. FeCoMnNi, and confirm that it shows a body-centered cubic structure and is stable at high temperatures.