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，以应对这些技术带来的日益复杂的科学和工程挑战。碳基（有机）半导体提供了满足甚至超过这些应用所需性能指标的机会。然而，对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速有机半导体的商业准备，ACROSS（加速有机半导体系统的商业准备）团队汇集了来自化学，物理，器件工程，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，ACROSS提供了一个独特的多学科环境，以培养下一代多样化和好奇的科学家和工程师，其中计算机和机器将对推动发现至关重要。技术说明：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。这些特性，再加上需要在大面积上的材料均匀性以及当并入器件中时的环境和操作稳定性，抑制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，ACROSS（加速有机半导体系统的商业准备）团队在材料基因组计划（MGI）的背景下汇集了材料合成和表征，机器信息和数据驱动的材料设计，理论以及设备开发和部署方面的专业知识，以加速有机半导体的商业准备。ACROSS的研究目标是：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，扩展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，以促进可在大面积上铺展的有机半导体的开发，具有统一和稳定的规定属性;以及，3）应用优化的有机半导体和工艺条件以在大面积上产生X射线检测器和薄膜晶体管（TFT）阵列。ACROSS的主要成果将是：1）扩展的材料发现数据基础设施和工具，重点是全球数据和工具访问的民主化; 2）开发软件，将有机半导体表征和器件数据捕获为结构化格式，以创建用于有机半导体性质预测的ML模型; 3）有机半导体，其针对高电荷载流子迁移率、辐射敏感性以及环境和操作稳定性进行了优化;并且，在本发明中，4）共同设计分子和工艺参数，生产适用于大面积应用的有机半导体，涵盖原型TFT和辐射剂量计（mm尺度）到TFT阵列和高分辨率辐射成像器（cm到m尺度）。此外，ACROSS将建立和建立管道，从我们附近的社区和机构招募和指导在STEM中代表性不足的本科生和研究生研究人员，使他们具备进入广泛的，跨学科技术和数据-该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的评估支持影响审查标准。

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.