MRI: Track 1 Acquisition of a university-wide computational cluster

该项目是为俄亥俄州大学（OHIO）采购一个计算机集群。俄亥俄州的研究人员将利用该集群筛选数百万种化合物对某些疾病的有效性，以筛选出少数可以在实验室中进一步测试的潜在治疗化合物。俄亥俄州的研究人员计划将该集群用于许多其他应用，例如改进电动汽车电池，增强飞机的导航辅助，自动筛选肿瘤的医学图像，减轻石油和天然气管道的生锈，从煤炭中产生环保建筑材料，以及识别人体中的异常基因。此外，该集群将被跨学科的教育工作者利用，使学生能够培养跨科学，工程和经济学领域所需的强大数据分析技能。俄亥俄州是英特尔阿巴拉契亚半导体教育和技术（ASCENT）生态系统的领先机构，旨在为半导体行业培训劳动力。ASCENT计划将依靠计算机集群来培训学生进行数据分析和机器学习。俄亥俄州目前缺乏自己的计算机集群，严重阻碍了依赖这种基础设施的研究和教学。 内部计算机集群将支持各种各样的研究，这些研究需要量子计算、分子模拟、机器学习、大数据分析、数值优化、图像分析、模式检测、天气模拟和有限元分析。该研究将带来重大的技术里程碑，包括但不限于设计新的腐蚀抑制剂，药物发现，将煤炭转化为绿色建筑材料来源，为电动汽车电池创造清洁的石墨来源，自动化医学图像分析以检测肿瘤，遗传学以及开发用于飞机导航的新技术。这套教材也将是一个重要的教学工具。随着大数据和机器学习的出现以及各种计算领域的快速发展，本科生和研究生必须在这些领域接受培训，以便他们的技能满足行业需求。俄亥俄州是ASCENT的领导机构，ASCENT是一个劳动力发展和培训计划，为俄亥俄州的半导体行业培养熟练的专业人员。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。

This project is to procure a computer cluster for Ohio University (OHIO). Researchers at OHIO will leverage the cluster to screen millions of chemical compounds for their effectiveness against some disease to shortlist a handful of potential therapeutic compounds that can be tested further in a laboratory. OHIO researchers plan to use the cluster for numerous other applications, such as improving electric car batteries, enhancing navigational aids of airplanes, automating screening of medical images for tumors, mitigating rusting in oil and gas pipelines, generating environmentally friendly construction materials from coal, and identifying abnormal genes in the human body. Additionally, the cluster will be utilized by educators across disciplines, allowing students to develop strong data analysis skills necessary in fields spanning sciences, engineering, and economics. OHIO is the leading institution for Intel Appalachian Semiconductor Education and Technical (ASCENT) Ecosystem that aims to train the workforce for the semiconductor industry. The ASCENT program will rely on the computer cluster to train the students in data analytics and machine learning. OHIO currently lacks its own computer cluster, significantly impeding research and teaching reliant on such infrastructure. An in-house computer cluster will enable a diverse variety of research that require quantum calculations, molecular simulations, machine learning, big data analysis, numerical optimization, image analysis, pattern detection, weather simulations, and finite element analysis. The research enabled will result in significant technological milestones, including but not limited to the design of new corrosion inhibitors, drug discovery, transforming coal to a green source of construction materials, creating a clean source of graphite for electric car batteries, automation of medical image analysis to detect tumors, genetics, and developing novel technologies for airplane navigation. The cluster will be an important teaching tool as well. With the advent of big data and machine learning as well as rapid developments in various computational fields, it is essential that undergraduate and graduate students are trained in these areas so that their skills meet the industry needs. OHIO is the lead institution for ASCENT, which is a workforce development and training program to cultivate skilled professionals for Ohio’s semiconductor industry. An in-house cluster will be pivotal for imparting training in database management systems, machine learning, algorithms, optimization, and other semiconductor industry-specific needs.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.