Collaborative Research: DMREF: Accelerating the Commercial Readiness of Organic Semiconductor Systems (ACROSS)

非技术描述：电子、显示器和照明、大面积发电和存储以及用于医疗和国家安全应用的传感器的未来需要不断开发新材料，以满足这些技术带来的日益复杂的科学和工程挑战。碳基(有机)半导体提供了达到甚至超过这些应用所需的性能指标的机会。然而，关于哪种有机半导体最适合这些应用以及如何最好地制造它们，仍然存在许多问题，因为它们本身可能很复杂。为了应对这些挑战并加快有机半导体的商业准备，VERS(加速有机半导体系统的商业准备)团队汇集了化学、物理、设备工程、材料科学以及建模和数据科学的专业知识，以加快特定应用的材料设计。值得注意的是，横跨提供了一个独特的、多学科的环境，以培养下一代多样化和好奇心强的科学家和工程师，其中计算机和机器将是推动发现的关键。技术描述：碳基有机半导体中电子和光学过程的多尺度和多参数性质是决定这些材料在各种技术中成功发挥作用的关键特征。这些特点，再加上需要大面积的材料一致性，以及在器件中整合时的环境和操作稳定性，阻碍了有机半导体在商业应用中的广泛采用。为了应对这一挑战，在材料基因组计划(MGI)的背景下，VERNAL(加速有机半导体系统的商业准备)团队汇集了材料合成和表征、机器信息和数据驱动的材料设计、理论以及器件开发和部署方面的专业知识，以加快有机半导体的商业准备。跨研究的目标是：1)通过扩展已经全面的数据基础设施和相关的机器学习(ML)基础设施，扩大机器知情的有机半导体设计工具和访问；2)共同设计分子和晶体/材料结构和工艺参数，以促进可分布在大面积且具有统一和稳定的规定性能的有机半导体的开发；以及，3)应用优化的有机半导体和工艺条件，创建大面积的X射线探测器和薄膜晶体管(TFT)阵列。Cross的主要成果将是：1)扩展数据基础设施和材料发现工具，重点是民主化全球数据和工具访问；2)开发用于捕获有机半导体表征和器件数据为结构化格式的软件，以便能够为有机半导体性质预测创建ML模型；3)针对高载流子迁移率、辐射敏感性和环境与操作稳定性进行优化的有机半导体；以及4)共同设计分子和工艺参数，以生产从TFT原型和辐射剂量计(毫米尺度)到TFT阵列和高分辨率辐射成像仪(厘米至米尺度)的大面积应用。此外，VERVE将建立和建立渠道，从我们附近的社区和机构招聘和指导在STEM中代表性不足的本科生和研究生研究人员，以使他们掌握进入一支广泛的、跨学科的技术和数据驱动的劳动力队伍所需的技术技能。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Non-technical Description: The future of electronics, displays and lighting, large-area power generation and storage, and sensors for medical and national security applications requires the continued development of new materials to meet the ever more complex scientific and engineering challenges presented by these technologies. Carbon-based (organic) semiconductors offer opportunities to meet and even exceed the performance metrics required for these applications. However, many questions remain as to which organic semiconductors best fit each of these applications and how best to make them, as they themselves can be complex. To tackle these challenges and accelerate the commercial readiness of organic semiconductors, the ACROSS (Accelerating the Commercial Readiness of Organic Semiconductor Systems) team brings together expertise from chemistry, physics, device engineering, materials science, and modeling and data science to speed up applications-specific materials design. Notably, ACROSS provides a distinct, multidisciplinary environment to train the next generation of diverse and inquisitive scientists and engineers, where computers and machines will be vital to drive discovery. Technical Description: The multi-scale and multi-parameter natures of electronic and optical processes in carbon-based organic semiconductors are critical features that determine successful function of these materials in a diverse array of technologies. These characteristics, coupled with the need for material uniformity over large areas and environmental and operational stability when incorporated in devices, inhibit the widespread adoption of organic semiconductors in commercial applications. To confront this challenge, the ACROSS (Accelerating the Commercial Readiness of Organic Semiconductor Systems) team brings together expertise, in the context of the Materials Genome Initiative (MGI), in materials synthesis and characterization, machine-informed and data-driven materials design, theory, and device development and deployment to accelerate the commercial readiness of organic semiconductors. The ACROSS research objectives are to: 1) Expand machine-informed organic semiconductor-design tools and access through extension of an already-comprehensive data infrastructure and associated machine-learning (ML) infrastructure; 2) Co-design molecular and crystal/material structures and processing parameters to facilitate the development of organic semiconductors spreadable over large areas with uniform and stable prescribed properties; and, 3) Apply optimized organic semiconductor and process conditions to create X-ray detectors and thin-film transistor (TFT) arrays across large areas. Key outcomes of ACROSS will be: 1) An expanded data infrastructure and tools for materials discovery with a focus on democratizing global data and tool access; 2) Software developed to capture organic semiconductor characterization and device data into structured formats to enable the creation of ML models for organic semiconductor property prediction; 3) Organic semiconductors optimized for high charge-carrier mobility, radiation sensitivity, and environmental and operational stability; and, 4) Co-designed molecular and processing parameters to produce organic semiconductors for large-area applications that span prototype TFTs and radiation dosimeters (mm scale) to TFT arrays and high-resolution radiation imagers (cm to m scale). Further, ACROSS will build on and establish pipelines to recruit and mentor undergraduate and graduate researchers who are underrepresented in STEM from our nearby communities and institutions to equip them with the technical skills required to enter a broad, interdisciplinary technical and data-driven workforce.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.