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MRI: Acquisition of a high-performance GPU-based computer for advanced multiscale materials modeling

MRI：购买基于 GPU 的高性能计算机，用于高级多尺度材料建模

基本信息

批准号：
1828629
负责人：
Juan De Pablo
金额：
$ 99.95万
依托单位：
University of Chicago
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1828629&HistoricalAwards=false
关键词：
MRI Acquisition performance GPU based

项目摘要

The funds from the Division of Materials Research support the University of Chicago to acquire a modern, Graphics Processor Unit (GPU) based computer that will allow researchers from a wide variety of disciplines to pursue new materials research directions that are based on the use of machine learning strategies and that could not be investigated without access to the necessary computational resources. Four focus areas are envisaged, namely (1) new materials discovery, (2) development of new molecular models, (3) machine-learning enabled interpretation of experiments, and (4) development of new simulation algorithms that rely on concepts from machine learning. A series of workshops and other outreach activities will be carried out to engage the broader materials research community in advanced computational research on GPU-accelerated computers. The Division of Materials Research supports the University of Chicago with the project "Acquisition of a high-performance GPU-based computer for advanced multiscale materials modeling" . The instrument is a high-performance GPU (Graphics Processing Unit) cluster tailored for fast and efficient simulations, including molecular dynamics (MD) simulations, hybrid particle-continuum simulations, mesoscale simulations and continuum simulations. This cluster will thrust forward a wide array of multidisciplinary research projects anchored in molecular engineering, physics, chemistry, and biology, and builds upon the investigators' involvement in developing the fastest, most powerful suite of simulation software for particle-based simulations on GPUs, including molecular dynamics simulations, evolutionary optimization algorithms, and powerful deep learning-assisted advanced sampling algorithms. That software is now distributed freely, and it is used throughout the world. The acquisition of a large GPU cluster at The University of Chicago will provide a unique computational resource and drive new collaborative efforts in algorithm and software development at the interface between molecular engineering, physics, chemistry, biology, computer science and materials science. The system will operate as a facility to serve four interrelated objectives, namely (i) support the local research community and their collaborators to achieve significant scientific advances on an array of collaborative research projects touching on proteins, membranes, cytoskeleton, fluids, colloids, nanoparticles, polymers, and mechanical metamaterials; (ii) create a vibrant interdisciplinary intellectual research community of theoreticians and experimentalists around the facility, and promote the creative development of novel and more effective advanced sampling algorithms, centered on the creation of a unified set of software tools supporting state-of-the-art methodologies; (iii) organize workshops to train undergraduate, graduate, and postgraduate students from a wide array of disciplines to disseminate knowledge about the utilization of distributed computational environments; (iv) contribute to diversifying the scientific workforce by organizing training and educational programs for undergraduate students from institutions serving under-represented minorities.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.

来自材料研究部的资金支持芝加哥大学获得基于图形处理器单元（GPU）的现代计算机，该计算机将允许来自各种学科的研究人员追求基于使用机器学习策略的新材料研究方向，并且如果没有获得必要的计算资源，则无法进行调查。设想了四个重点领域，即（1）新材料发现，（2）新分子模型的开发，（3）机器学习使实验的解释，以及（4）依赖于机器学习概念的新模拟算法的开发。将举办一系列讲习班和其他外联活动，使更广泛的材料研究界参与GPU加速计算机的高级计算研究。材料研究部支持芝加哥大学的项目“采购基于GPU的高性能计算机，用于先进的多尺度材料建模”。该仪器是一个高性能的GPU（图形处理单元）集群，专为快速高效的模拟量身定制，包括分子动力学（MD）模拟，混合粒子连续模拟，中尺度模拟和连续模拟。该集群将推进一系列多学科研究项目，包括分子工程、物理、化学和生物学，并建立在研究人员参与开发最快、最强大的模拟软件套件的基础上，用于GPU上基于粒子的模拟，包括分子动力学模拟、进化优化算法和强大的深度学习辅助高级采样算法。该软件现在免费分发，并在世界各地使用。收购芝加哥大学的大型GPU集群将提供独特的计算资源，并在分子工程、物理学、化学、生物学、计算机科学和材料科学之间的接口上推动算法和软件开发方面的新合作。该系统将作为一个设施，以服务四个相互关联的目标，即（i）支持本地研究界及其合作者在一系列涉及蛋白质、膜、细胞骨架、流体、胶体、纳米颗粒、聚合物和机械超材料的合作研究项目上取得重大科学进展; ㈡在该设施周围建立一个由理论家和实验家组成的充满活力的跨学科智力研究社区，并促进创造性地开发新颖和更有效的先进取样算法，以创建一套统一的软件工具为中心，支持最先进的方法; ㈢组织讲习班，培训来自各种学科的本科生、研究生和研究生，传播关于利用分布式计算环境的知识;（iv）通过为来自以下机构的本科生组织培训和教育方案，促进科学劳动力的多样化：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（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Prolate and oblate chiral liquid crystal spheroids

DOI：
10.1126/sciadv.aba6728
发表时间：
2020-07-01
期刊：
SCIENCE ADVANCES
影响因子：
13.6
作者：
Sadati, Monirosadat;Martinez-Gonzalez, Jose A.;de Pablo, Juan J.
通讯作者：
de Pablo, Juan J.

Motile dislocations knead odd crystals into whorls

DOI：
10.1038/s41567-021-01429-3
发表时间：
2021-12-16
期刊：
NATURE PHYSICS
影响因子：
19.6
作者：
Bililign, Ephraim S.;Usabiaga, Florencio Balboa;Irvine, William T. M.
通讯作者：
Irvine, William T. M.

Crossover from Rouse to Reptation Dynamics in Salt-Free Polyelectrolyte Complex Coacervates

无盐聚电解质复合凝聚层中从唤醒到蠕动动力学的交叉

DOI：
10.1021/acsmacrolett.0c00522
发表时间：
2020
期刊：
ACS Macro Letters
影响因子：
7.015
作者：
Yu, Boyuan;Rauscher, Phillip M.;Jackson, Nicholas E.;Rumyantsev, Artem M.;de Pablo, Juan J.
通讯作者：
de Pablo, Juan J.

Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation