EAGER: Type II: Deep Learning and Combinatorial Algorithms for Inorganic Crystal Structure Prediction

EAGER：类型 II：无机晶体结构预测的深度学习和组合算法

• 批准号: 1843025
• 负责人: Sanguthevar Rajasekaran
• 金额: $ 30万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1843025&HistoricalAwards=false
• 关键词: EAGER; Type; II; Deep; Learning

NONTECHNICAL SUMMARYThis EAGER award supports research and education involving a new collaboration kindled at the MATDAT18 Datathon event focused on developing artificial intelligence methods to discover new materials or identify specific materials with desired properties for an application. Methods involving computation, materials data, and the tools of data science offer the potential to find or design a material with desired properties much faster and at lower cost than traditional methods used by materials scientists and engineers. In this project, the research team will develop novel machine learning techniques to mine knowledge from various publicly available databases on materials and their properties. The knowledge thus gained can be utilized for material selection and design. The team will focus first on using the methods of data science and materials data from a large community repository obtained from using computers and theory to calculate the energy needed to form a material from its constitutive elements. All the techniques developed in this project will be coded as software for different computers. Software will be released as open source codes to the materials science and data science communities via a number of mechanisms including the GitHub. This project will also provide educational opportunities to graduate and undergraduate students and a first-hand research experience in data analysis for materials science. Results of this project will be incorporated in appropriate undergraduate and graduate courses. Strong efforts will be made to include minorities and women. Results of this project will be disseminated widely via publications in journals and international conferences.TECHNICAL SUMMARYThis EAGER award supports research and education involving a new collaboration kindled at the MATDAT18 Datathon event focused on developing deep learning predictors for formation energies and other materials properties. Large databases of computed material properties, such as The Materials Project and AFLOWLIB developed under Materials Genome Initiative, host properties of tens of thousands of materials. They are primarily employed to screen materials for various target applications such as photocatalysis and battery materials. Such databases can also be utilized to develop deep learning-based predictors of materials properties. These predictions are expected to be more accurate than predictions made using traditional machine learning (ML) techniques. Even cutting edge conventional ML methods such as Gradient Boosting or Random Forest of Trees have limited capacity, or the ability to learn, when compared to multi-layer deep artificial neural networks employed in deep learning to mine vast data. In this project the research team aims to develop a deep learning predictor for formation energy of crystals. The investigators also propose to develop other relevant combinatorial algorithms for solving this problem. Formation energy, which is the energy difference between the crystal and the constituent elements in their atomic form, is one of the most reliable properties available from these databases. The focus of this project is on fast and highly accurate prediction of formation energies and stability of materials by utilizing the superior capacity of deep learning systems and other algorithms to learn from big data.The project will deliver a publicly accessible cyber infrastructure implementing a deep learning system capable of predicting formation energies for inorganic materials with an accuracy that is vastly superior to that of the predictors built with traditional ML models, and new forms of chemical representations of materials that can be reused to predict other properties of materials. One of the challenges in the employment of deep learning techniques is in the large training times taken by these algorithms. The research team plans to address this challenge with a variety of algorithmic innovations including novel parallel training algorithms. The investigators plan to employ a number of parallel architectures including CPU clusters and GPUs.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.

EAGER奖支持研究和教育，涉及在MATDAT18 Datasheet活动中点燃的新合作，专注于开发人工智能方法，以发现新材料或识别具有应用所需特性的特定材料。 涉及计算，材料数据和数据科学工具的方法提供了比材料科学家和工程师使用的传统方法更快，成本更低的找到或设计具有所需特性的材料的可能性。在这个项目中，研究小组将开发新的机器学习技术，从各种公开的材料及其属性数据库中挖掘知识。由此获得的知识可用于材料选择和设计。 该团队将首先专注于使用数据科学方法和来自大型社区存储库的材料数据，这些数据是通过使用计算机和理论来计算从其构成元素形成材料所需的能量。 在这个项目中开发的所有技术将被编码为不同计算机的软件。软件将通过包括GitHub在内的多种机制作为开源代码发布给材料科学和数据科学社区。该项目还将为研究生和本科生提供教育机会，并提供材料科学数据分析的第一手研究经验。该项目的成果将纳入适当的本科和研究生课程。将作出巨大努力，将少数民族和妇女包括在内。该项目的成果将通过期刊和国际会议的出版物广泛传播。技术总结该EAGER奖支持研究和教育，涉及在MATDAT18 Datasheet活动中点燃的新合作，专注于开发形成能量和其他材料属性的深度学习预测器。计算材料属性的大型数据库，如在材料基因组计划下开发的材料项目和AFLOWLIB，拥有数万种材料的属性。它们主要用于筛选各种目标应用的材料，如电池和电池材料。这些数据库也可以用于开发基于深度学习的材料性能预测器。预计这些预测比使用传统机器学习（ML）技术进行的预测更准确。与深度学习中用于挖掘大量数据的多层深度人工神经网络相比，即使是最先进的传统ML方法，如梯度提升或随机树森林，其容量或学习能力也有限。在这个项目中，研究小组的目标是开发一个深度学习预测晶体形成能量的方法。研究人员还建议开发其他相关的组合算法来解决这个问题。形成能是晶体和原子形式的组成元素之间的能量差，是这些数据库中最可靠的属性之一。该项目的重点是利用深度学习系统和其他算法从大数据中学习的上级能力，快速和高精度地预测材料的形成能和稳定性。该项目将提供一个可公开访问的网络基础设施，该基础设施实施了一个能够预测无机材料形成能的深度学习系统，其准确性远远上级所构建的预测器传统的ML模型，以及新形式的材料化学表示，可以重复使用来预测材料的其他属性。使用深度学习技术的挑战之一是这些算法需要大量的训练时间。研究团队计划通过各种算法创新来应对这一挑战，包括新颖的并行训练算法。该奖项反映了NSF的法定使命，并通过使用基金会的智力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

DTWNet: a Dynamic Time Warping Network

• 发表时间: 2019
• 期刊: Nucleic Acids Research
• 影响因子: 14.9
• 作者: Xingyu Cai;Tingyang Xu;Jinfeng Yi;Junzhou Huang;S. Rajasekaran

HMSC: a Hybrid Metagenomic Sequence Classification Algorithm

HMSC：混合宏基因组序列分类算法

• DOI: 10.1145/3388440.3412468
• 发表时间: 2020
• 期刊: Computational Biology and Health Informatics
• 作者: Saha, Subrata;Wang, Zigeng;Rajasekaran, Sanguthevar
• 通讯作者: Rajasekaran, Sanguthevar

TAG: Gradient Attack on Transformer-based Language Models

• DOI: 10.18653/v1/2021.findings-emnlp.305
• 发表时间: 2021-03
• 作者: Jieren Deng;Yijue Wang;Ji Li;Chenghong Wang;Chao Shang;Hang Liu;S. Rajasekaran;Caiwen Ding

Machine Learning Techniques in Structure-Property Optimization of Polymeric Scaffolds for Tissue Engineering

组织工程聚合物支架结构性能优化中的机器学习技术

• DOI: 10.29007/nxm3
• 发表时间: 2022
• 期刊: EPiC Series in Computing
• 作者: Wang, Zigeng;Xiao, Xia;Nukavarapu, Syam;Kumbar, Sangamesh;Rajasekaran, Sanguthevar
• 通讯作者: Rajasekaran, Sanguthevar

Against Membership Inference Attack: Pruning is All You Need

• DOI: 10.24963/ijcai.2021/432
• 发表时间: 2020-08
• 作者: Yijue Wang;Chenghong Wang;Zigeng Wang;Shangli Zhou;Hang Liu;J. Bi;Caiwen Ding;S. Rajasekaran