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机器学习揭示了材料中的轨道相互作用。

Machine learning reveals orbital interaction in materials.

作者信息

Lam Pham Tien, Kino Hiori, Terakura Kiyoyuki, Miyake Takashi, Tsuda Koji, Takigawa Ichigaku, Chi Dam Hieu

机构信息

Japan Advanced Institute of Science and Technology, Nomi, Japan.

Elements Strategy Initiative Center for Magnetic Materials, National Institute for Materials Science, Tsukuba, Japan.

出版信息

Sci Technol Adv Mater. 2017 Oct 26;18(1):756-765. doi: 10.1080/14686996.2017.1378060. eCollection 2017.

DOI:10.1080/14686996.2017.1378060
PMID:29152012
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5678453/
Abstract

We propose a novel representation of materials named an 'orbital-field matrix (OFM)', which is based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Experimental investigation shows that the formation energies of crystalline materials, atomization energies of molecular materials, and local magnetic moments of the constituent atoms in bimetal alloys of lanthanide metal and transition-metal can be predicted with high accuracy using the OFM. Knowledge regarding the role of the coordination numbers of the transition-metal and lanthanide elements in determining the local magnetic moments of the transition-metal sites can be acquired directly from decision tree regression analyses using the OFM.

摘要

我们提出了一种名为“轨道场矩阵(OFM)”的材料新表示法,它基于价层电子的分布。我们证明这种新表示法在挖掘材料数据方面非常有用。实验研究表明,使用OFM可以高精度地预测晶体材料的形成能、分子材料的原子化能以及镧系金属与过渡金属双金属合金中组成原子的局部磁矩。关于过渡金属和镧系元素的配位数在确定过渡金属位点局部磁矩中的作用的知识,可以直接从使用OFM的决策树回归分析中获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/deb9/5678453/a9d8da401ffd/TSTA_A_1378060_F0004_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/deb9/5678453/9e4835653e7c/TSTA_A_1378060_UF0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/deb9/5678453/4168f9f7b6bc/TSTA_A_1378060_F0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/deb9/5678453/93baa8410a1d/TSTA_A_1378060_F0002_B.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/deb9/5678453/0a828bd5e4c0/TSTA_A_1378060_F0003_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/deb9/5678453/a9d8da401ffd/TSTA_A_1378060_F0004_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/deb9/5678453/9e4835653e7c/TSTA_A_1378060_UF0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/deb9/5678453/4168f9f7b6bc/TSTA_A_1378060_F0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/deb9/5678453/93baa8410a1d/TSTA_A_1378060_F0002_B.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/deb9/5678453/0a828bd5e4c0/TSTA_A_1378060_F0003_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/deb9/5678453/a9d8da401ffd/TSTA_A_1378060_F0004_OC.jpg

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1
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2
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Phys Chem Chem Phys. 2016 May 18;18(20):13754-69. doi: 10.1039/c6cp00415f.
3
Big data of materials science: critical role of the descriptor.材料科学大数据:描述符的关键作用。
机器学习预测超导材料化学成分的临界温度。
J Chem Inf Model. 2024 Oct 14;64(19):7349-7375. doi: 10.1021/acs.jcim.4c01137. Epub 2024 Sep 17.
4
Unraveling the Stability of Layered Intercalation Compounds through First-Principles Calculations: Establishing a Linear Free Energy Relationship with Aqueous Ions.通过第一性原理计算揭示层状插层化合物的稳定性:建立与水合离子的线性自由能关系。
ACS Phys Chem Au. 2024 Mar 7;4(3):281-291. doi: 10.1021/acsphyschemau.3c00063. eCollection 2024 May 22.
5
Pretraining Strategies for Structure Agnostic Material Property Prediction.结构不可知材料属性预测的预训练策略。
J Chem Inf Model. 2024 Feb 12;64(3):627-637. doi: 10.1021/acs.jcim.3c00919. Epub 2024 Feb 1.
6
Evidence-based recommender system for high-entropy alloys.基于证据的高熵合金推荐系统。
Nat Comput Sci. 2021 Jul;1(7):470-478. doi: 10.1038/s43588-021-00097-w. Epub 2021 Jul 19.
7
Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization.用于利用热能的机器学习:从材料发现到系统优化
ACS Energy Lett. 2022 Oct 14;7(10):3204-3226. doi: 10.1021/acsenergylett.2c01836. Epub 2022 Sep 1.
8
Understanding and optimization of hard magnetic compounds from first principles.基于第一性原理对硬磁化合物的理解与优化。
Sci Technol Adv Mater. 2021 Sep 15;22(1):543-556. doi: 10.1080/14686996.2021.1935314. eCollection 2021.
9
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.结合机器学习和计算化学,对化学系统进行预测性洞察。
Chem Rev. 2021 Aug 25;121(16):9816-9872. doi: 10.1021/acs.chemrev.1c00107. Epub 2021 Jul 7.
10
Explainable machine learning for materials discovery: predicting the potentially formable Nd-Fe-B crystal structures and extracting the structure-stability relationship.用于材料发现的可解释机器学习:预测潜在可形成的钕铁硼晶体结构并提取结构-稳定性关系。
IUCrJ. 2020 Sep 23;7(Pt 6):1036-1047. doi: 10.1107/S2052252520010088. eCollection 2020 Nov 1.
Phys Rev Lett. 2015 Mar 13;114(10):105503. doi: 10.1103/PhysRevLett.114.105503. Epub 2015 Mar 10.
4
Data mining for materials design: a computational study of single molecule magnet.
J Chem Phys. 2014 Jan 28;140(4):044101. doi: 10.1063/1.4862156.
5
Understanding the composition and activity of electrocatalytic nanoalloys in aqueous solvents: a combination of DFT and accurate neural network potentials.理解水相溶剂中电催化纳米合金的组成和活性:DFT 和精确神经网络势的结合。
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6
Accelerating materials property predictions using machine learning.利用机器学习加速材料性能预测。
Sci Rep. 2013 Sep 30;3:2810. doi: 10.1038/srep02810.
7
Finding density functionals with machine learning.利用机器学习寻找密度泛函。
Phys Rev Lett. 2012 Jun 22;108(25):253002. doi: 10.1103/PhysRevLett.108.253002. Epub 2012 Jun 19.
8
Microscopic origins of the anomalous melting behavior of sodium under high pressure.高压下钠反常熔融行为的微观起源。
Phys Rev Lett. 2012 Mar 16;108(11):115701. doi: 10.1103/PhysRevLett.108.115701. Epub 2012 Mar 13.
9
Fast and accurate modeling of molecular atomization energies with machine learning.利用机器学习实现分子原子化能的快速、精确建模。
Phys Rev Lett. 2012 Feb 3;108(5):058301. doi: 10.1103/PhysRevLett.108.058301. Epub 2012 Jan 31.
10
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