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Theory-Guided Discovery of Tin-Based Materials

锡基材料的理论引导发现

基本信息

批准号：
1821815
负责人：
Alexey Kolmogorov
金额：
$ 35.41万
依托单位：
SUNY at Binghamton
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1821815&HistoricalAwards=false
关键词：
Theory Guided Discovery Tin Based

项目摘要

NON-TECHNICAL SUMMARYThis award supports computational and theoretical research to advance theory-guided discovery of new materials through rapid search of the large space of chemical compositions. The central aims of this project are to perform a systematic screening of tin alloys and develop a library of neural network-based interatomic potentials for an extended set of chemical elements. The neural network models will be used to accelerate the search over possible structures at the level of atoms. Research into tin alloys has attracted renewed interest due to their potential to display novel physics and next-generation functional features. Finely tuned tin-based topological insulators could find future use in spintronics, quantum computing, and thermoelectric materials which can generate electricity from heat. High-capacity tin-based electrodes with improved durability could make batteries cheaper and safer. Carefully optimized tin-based solders may reduce the use of toxic lead-containing materials. This project includes educational activities to attract young students and members of underrepresented groups to scientific research. The PI will contribute a new theme to Binghamton University's Physics Outreach Program for middle school students, develop a set of hands-on presentations on neural networks for undergraduate students, and recruit students from different majors enrolled in Binghamton University's Evolutionary Studies program to carry out interdisciplinary undergraduate research.TECHNICAL SUMMARYThis award supports computational and theoretical research to advance theory-guided discovery of new materials through rapid search of the large space of chemical compositions. The PI will focus on the systematic study of tin-based materials. The work is motivated by the remarkable richness of the tin alloys' structural and electronic properties enabling their use as topological insulators, battery anodes, solders, and more. The main challenges associated with the study and development of tin alloys lie in the complexity of their structures and the diversity of their bonding mechanisms. These factors limit the scope of ab initio-based study and the accuracy of classical potential-based modeling. The PI aims to demonstrate that the efficiency and reliability of materials prediction can be improved considerably by: (i) screening a large materials class with a suite of diverse search strategies, and (ii) using emerging neural network methodology for modeling interatomic interactions to accelerate the search. For the comprehensive sampling of the configuration space of structures and compositions, the research team will rely on a combination of high-throughput, evolutionary, and rational design searches. Identification of new tin-based wide-gap topological insulators, durable battery anodes, and stable lead-free solders will advance knowledge in several areas of basic and application-driven research. For the systematic construction of reusable neural network models, the research team will use a recently developed stratified training procedure enabling a natural extension of libraries to larger sets of chemical systems. The neural network models will be freely available as a part of the group's open-source MAISE package. This effort will promote the development and application of emerging machine learning methods in materials research. The scientific work will be integrated with educational and outreach activities which will foster the interest of the next generation in science, technology, engineering, and mathematics disciplines.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项支持计算和理论研究，通过快速搜索化学成分的大空间来推进理论指导的新材料发现。该项目的中心目标是对锡合金进行系统的筛选，并为一组扩展的化学元素开发基于神经网络的原子间势库。神经网络模型将用于加速在原子水平上对可能结构的搜索。锡合金的研究吸引了新的兴趣，因为它们有可能显示新的物理和下一代功能特性。精细调谐的锡基拓扑绝缘体可以在自旋电子学，量子计算和热电材料中找到未来的用途，这些材料可以从热量中发电。具有更好耐用性的高容量锡基电极可以使电池更便宜，更安全。仔细优化的锡基焊料可以减少有毒含铅材料的使用。该项目包括开展教育活动，吸引青年学生和代表性不足群体的成员参加科学研究。PI将为宾厄姆顿大学的中学生物理外展计划贡献一个新的主题，为本科生开发一套关于神经网络的动手演示，并招收就读于宾厄姆顿大学进化研究项目的不同专业的学生，开展跨学科的本科研究。技术总结该奖项支持计算和理论研究，以推进理论-通过快速搜索化学成分的大空间来指导新材料的发现。PI将专注于锡基材料的系统研究。这项工作的动机是锡合金的结构和电子特性的显着丰富，使其能够用作拓扑绝缘体，电池阳极，焊料等。与锡合金的研究和开发相关的主要挑战在于其结构的复杂性和其结合机制的多样性。这些因素限制了从头算研究的范围和经典势基模型的准确性。PI旨在证明材料预测的效率和可靠性可以通过以下方式大大提高：（i）使用一套不同的搜索策略筛选大型材料类别，以及（ii）使用新兴的神经网络方法来建模原子间相互作用以加速搜索。对于结构和组合物的配置空间的全面采样，研究团队将依赖于高通量，进化和合理设计搜索的组合。新的锡基宽间隙拓扑绝缘体、耐用的电池阳极和稳定的无铅焊料的鉴定将推进基础和应用驱动研究的几个领域的知识。为了系统地构建可重复使用的神经网络模型，研究团队将使用最近开发的分层训练程序，使库能够自然扩展到更大的化学系统。神经网络模型将作为该组织开源MAISE包的一部分免费提供。这一努力将促进新兴机器学习方法在材料研究中的发展和应用。科学工作将与教育和推广活动相结合，以培养下一代对科学、技术、工程和数学学科的兴趣。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Toward ab Initio Ground States of Gold Clusters via Neural Network Modeling

DOI：
10.1021/acs.jpcc.9b08517
发表时间：
2019-11
期刊：
The Journal of Physical Chemistry C
影响因子：
0
作者：
Aidan Thorn;J. Rojas-Nunez;S. Hajinazar;S. Baltazar;A. Kolmogorov
通讯作者：
Aidan Thorn;J. Rojas-Nunez;S. Hajinazar;S. Baltazar;A. Kolmogorov

Complex pressure-temperature structural phase diagram of the honeycomb iridate Cu2IrO3

蜂窝状铱酸盐 Cu2IrO3 的复杂压力-温度结构相图

DOI：
10.1103/physrevb.104.014102
发表时间：
2021
期刊：
Physical Review B
影响因子：
3.7
作者：
Fabbris, G.;Thorn, A.;Bi, W.;Abramchuk, M.;Bahrami, F.;Kim, J. H.;Shinmei, T.;Irifune, T.;Tafti, F.;Kolmogorov, A. N.
通讯作者：
Kolmogorov, A. N.

Prediction of stable Li-Sn compounds: boosting ab initio searches with neural network potentials