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

本项目是为俄亥俄大学（Ohio）采购一个计算机集群。俄亥俄大学的研究人员将利用这一集群来筛选数百万种化合物，以确定它们对某些疾病的有效性，从而筛选出少数几种潜在的治疗化合物，这些化合物可以在实验室中进行进一步的测试。俄亥俄大学的研究人员计划将该集群用于许多其他应用，如改进电动汽车电池、增强飞机导航设备、肿瘤医学图像的自动筛选、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，使学生能够在科学、工程和经济等领域培养必要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项目将依靠计算机集群对学生进行数据分析和机器学习方面的培训。俄亥俄州目前缺乏自己的计算机集群，这极大地阻碍了对此类基础设施的研究和教学依赖。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。这项研究将带来重大的技术里程碑，包括但不限于设计新的缓蚀剂、药物发现、将煤炭转化为绿色建筑材料、为电动汽车电池创造清洁的石墨来源、用于检测肿瘤的医学图像分析自动化、遗传学和开发飞机导航的新技术。集群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，本科生和研究生在这些领域接受培训是必不可少的，这样他们的技能才能满足行业需求。俄亥俄州是ASCENT的领导机构，ASCENT是一项劳动力发展和培训计划，旨在为俄亥俄州的半导体行业培养熟练的专业人员。内部集群对于传授数据库管理系统、机器学习、算法、优化和其他半导体行业特定需求方面的培训至关重要。该奖项反映了美国国家科学基金会的法定使命，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。

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.