• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

挖掘离子取代数据以发现新化合物。

Data mined ionic substitutions for the discovery of new compounds.

机构信息

Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

出版信息

Inorg Chem. 2011 Jan 17;50(2):656-63. doi: 10.1021/ic102031h. Epub 2010 Dec 13.

DOI:10.1021/ic102031h
PMID:21142147
Abstract

The existence of new compounds is often postulated by solid state chemists by replacing an ion in the crystal structure of a known compound by a chemically similar ion. In this work, we present how this new compound discovery process through ionic substitutions can be formulated in a mathematical framework. We propose a probabilistic model assessing the likelihood for ionic species to substitute for each other while retaining the crystal structure. This model is trained on an experimental database of crystal structures, and can be used to quantitatively suggest novel compounds and their structures. The predictive power of the model is demonstrated using cross-validation on quaternary ionic compounds. The different substitution rules embedded in the model are analyzed and compared to some of the traditional rules used by solid state chemists to propose new compounds (e.g., ionic size).

摘要

固态化学家通常通过用化学性质相似的离子取代已知化合物晶体结构中的离子来假设新化合物的存在。在这项工作中,我们提出了如何通过离子取代将这种新化合物的发现过程表述在一个数学框架中。我们提出了一个概率模型,评估离子物种相互取代的可能性,同时保持晶体结构。该模型在晶体结构的实验数据库上进行训练,可以用于定量地提出新的化合物及其结构。通过对四元离子化合物的交叉验证,证明了该模型的预测能力。分析了模型中嵌入的不同取代规则,并将其与固态化学家用于提出新化合物的一些传统规则(例如离子大小)进行了比较。

相似文献

1
Data mined ionic substitutions for the discovery of new compounds.挖掘离子取代数据以发现新化合物。
Inorg Chem. 2011 Jan 17;50(2):656-63. doi: 10.1021/ic102031h. Epub 2010 Dec 13.
2
Understanding Li diffusion in Li-intercalation compounds.理解锂离子在插层化合物中的扩散。
Acc Chem Res. 2013 May 21;46(5):1216-25. doi: 10.1021/ar200329r. Epub 2012 May 14.
3
Information-theoretic approach for the discovery of design rules for crystal chemistry.信息论方法在晶体化学设计规则发现中的应用。
J Chem Inf Model. 2012 Jul 23;52(7):1812-20. doi: 10.1021/ci200628z. Epub 2012 Jul 12.
4
Volumes of solid state ions and their estimation.固态离子的体积及其估算。
Inorg Chem. 2005 Sep 5;44(18):6359-72. doi: 10.1021/ic048341r.
5
How evolutionary crystal structure prediction works--and why.进化晶体结构预测的工作原理——以及原因。
Acc Chem Res. 2011 Mar 15;44(3):227-37. doi: 10.1021/ar1001318. Epub 2011 Mar 1.
6
Data mining approaches to high-throughput crystal structure and compound prediction.用于高通量晶体结构和化合物预测的数据挖掘方法。
Top Curr Chem. 2014;345:139-79. doi: 10.1007/128_2013_486.
7
A probabilistic model for mining implicit 'chemical compound-gene' relations from literature.一种从文献中挖掘隐含“化合物-基因”关系的概率模型。
Bioinformatics. 2005 Sep 1;21 Suppl 2:ii245-51. doi: 10.1093/bioinformatics/bti1141.
8
Validation of the existence of tetrameric species of potassium trimethylsilanolate in the gas phase with a theoretical cluster model: role of the counterion as charge localizer in the structure.用理论团簇模型验证气相中三甲基硅酸钾四聚体物种的存在:抗衡离子在结构中作为电荷定位剂的作用。
J Phys Chem A. 2007 Apr 5;111(13):2629-33. doi: 10.1021/jp0686240. Epub 2007 Mar 14.
9
Influence of the ionic liquid/gas surface on ionic liquid chemistry.离子液体/气相界面对离子液体化学的影响。
Phys Chem Chem Phys. 2012 Apr 21;14(15):5071-89. doi: 10.1039/c2cp23851a. Epub 2012 Feb 20.
10
Ion conductivity and transport by porous coordination polymers and metal-organic frameworks.多孔配位聚合物和金属有机骨架的离子电导率和输运。
Acc Chem Res. 2013 Nov 19;46(11):2376-84. doi: 10.1021/ar300291s. Epub 2013 Jun 3.

引用本文的文献

1
Accelerated data-driven materials science with the Materials Project.借助材料项目实现加速的数据驱动型材料科学。
Nat Mater. 2025 Jul 3. doi: 10.1038/s41563-025-02272-0.
2
Mapping cation-eutaxy ternary with a phenomenological model.用现象学模型绘制阳离子共序三元图。
Nat Commun. 2025 Jul 1;16(1):5634. doi: 10.1038/s41467-025-60739-9.
3
AI-Driven Defect Engineering for Advanced Thermoelectric Materials.用于先进热电材料的人工智能驱动缺陷工程
Adv Mater. 2025 Sep;37(35):e2505642. doi: 10.1002/adma.202505642. Epub 2025 Jun 23.
4
Wide-ranging predictions of new stable compounds powered by recommendation engines.由推荐引擎推动的对新型稳定化合物的广泛预测。
Sci Adv. 2025 Jan 3;11(1):eadq1431. doi: 10.1126/sciadv.adq1431.
5
Exploring new useful phosphors by combining experiments with machine learning.通过将实验与机器学习相结合来探索新型实用荧光粉。
Sci Technol Adv Mater. 2024 Nov 7;25(1):2421761. doi: 10.1080/14686996.2024.2421761. eCollection 2024.
6
Exploring the diverse applications of sol-gel synthesized CaO:MgAlO nanocomposite: morphological, photocatalytic, and electrochemical perspectives.探索溶胶-凝胶合成的CaO:MgAlO纳米复合材料的多样应用:形态学、光催化及电化学视角
Discov Nano. 2024 Sep 12;19(1):147. doi: 10.1186/s11671-024-04093-7.
7
Self-supervised generative models for crystal structures.用于晶体结构的自监督生成模型。
iScience. 2024 Aug 6;27(9):110672. doi: 10.1016/j.isci.2024.110672. eCollection 2024 Sep 20.
8
Cation-eutaxy-enabled III-V-derived van der Waals crystals as memristive semiconductors.作为忆阻半导体的阳离子优序化 III-V 族衍生范德华晶体。
Nat Mater. 2024 Oct;23(10):1402-1410. doi: 10.1038/s41563-024-01986-x. Epub 2024 Aug 28.
9
Crystal Composition Transformer: Self-Learning Neural Language Model for Generative and Tinkering Design of Materials.晶体成分变换器:用于材料生成与改进设计的自学习神经语言模型
Adv Sci (Weinh). 2024 Sep;11(36):e2304305. doi: 10.1002/advs.202304305. Epub 2024 Aug 5.
10
Advancing materials science through next-generation machine learning.通过下一代机器学习推动材料科学发展。
Curr Opin Solid State Mater Sci. 2024 Jun;30. doi: 10.1016/j.cossms.2024.101157. Epub 2024 Apr 3.