CAREER: Computational transformation of organic photovoltaics manufacturing

职业：有机光伏制造的计算转型

• 批准号: 1653954
• 负责人: Eric Jankowski
• 金额: $ 50.25万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1653954&HistoricalAwards=false
• 关键词: CAREER; Computational; transformation; organic; photovoltaics

Title: CAREER: Computational transformation of organic photovoltaics manufacturingProposal 1653954: Jankowski, EricOrganic solar cells or organic photovoltaics (OPVs) are primarily plastic based films that offer a low-cost route to renewable electricity. Manufacturing organic photovoltaics is similar to newspaper printing and makes flexible, lightweight cells that can be incorporated into fabrics and curved surfaces. Their thin film configuration allows creative integration into building envelopes, expanding the opportunity to use solar energy for electricity generation in a wider range of infrastructure and building applications. This research project could contribute towards low-cost organic photovoltaics. A main barrier for use of this technology is that OPVs currently have lower efficiency compared to photovoltaics made with primarily inorganic materials (e.g. silicon). The research goal of this CAREER project is to control the structure of plastic solar cells in order to revolutionize sustainable energy generation. This research will use advanced computer simulations to understand how molecules used in organic photovoltaics can be arranged into nanostructures that are good at converting sunlight into electricity. By determining the molecules and conditions that robustly form favorable nanostructures, this project will improve recipes towards making solar cell systems. The project will facilitate regional benefits through a Boise State University service-learning project with the non-profit Discovery Center of Idaho, the state's only public hands-on science museum. Here, university students will develop exhibits as part of class and will engage directly with children and their families. The project will also include integrating computational education into the Boise State materials engineering curriculum. Together, these efforts will enhance participation, retention, diversity, and preparedness of university engineering students.Thermodynamic self-assembly offers a path to engineer the nanostructure of organic photovoltaic active layers, but it is not yet known which structures are best or which ingredients best assemble them. This fundamental engineering science research project employs high performance computing to screen thousands of potential ingredient combinations for those with the best structures. The project team will use coarse-grained molecular dynamics simulations accelerated with graphics processing units to predict experimentally relevant morphologies. Researchers will use atomistic configurations derived from the coarse morphologies to generate electronic structures from first principles calculations and to inform kinetic Monte Carlo simulations of charge mobility. Fundamental knowledge in the thermodynamics and kinetics of self-assembly of organic structures will be generated. Finally, to validate manufacturing protocols, the team will fabricate and characterize ingredients predicted to robustly assemble into high-efficiency structures in OPV solar cells in collaboration with National Renewable Energy Laboratory researchers.

职务名称：职业：有机光伏制造的计算转换提案1653954：Jankowski，EricOrganic太阳能电池或有机光伏（OPV）主要是塑料薄膜，为可再生电力提供了一条低成本的途径。制造有机光致发光材料类似于报纸印刷，可以制造出柔性、轻质的电池，可以融入织物和曲面中。它们的薄膜配置允许创造性地集成到建筑围护结构中，扩大了在更广泛的基础设施和建筑应用中使用太阳能发电的机会。该研究项目可能有助于低成本的有机光化学。使用该技术的一个主要障碍是，与主要由无机材料（例如硅）制成的光伏电池相比，OPV目前的效率较低。该项目的研究目标是控制塑料太阳能电池的结构，以彻底改变可持续能源的产生。这项研究将使用先进的计算机模拟来了解有机光化学中使用的分子如何排列成善于将阳光转化为电能的纳米结构。通过确定形成有利纳米结构的分子和条件，该项目将改进制造太阳能电池系统的配方。该项目将通过博伊西州立大学与该州唯一的公共动手科学博物馆爱达荷州非营利发现中心的服务学习项目促进区域利益。在这里，大学生将开发展品作为课堂的一部分，并将直接与儿童及其家人接触。该项目还将包括将计算教育纳入博伊西州材料工程课程。这些努力将共同提高大学工程专业学生的参与度、保留率、多样性和准备程度。热力学自组装为设计有机光伏活性层的纳米结构提供了一条途径，但目前还不知道哪种结构最好，哪种成分最能组装它们。这个基础工程科学研究项目采用高性能计算来筛选数千种具有最佳结构的潜在成分组合。该项目团队将使用粗粒度的分子动力学模拟与图形处理单元加速预测实验相关的形态。研究人员将使用来自粗糙形态的原子构型，从第一原理计算中生成电子结构，并为电荷迁移率的动力学蒙特卡罗模拟提供信息。在热力学和动力学的有机结构自组装的基础知识将产生。最后，为了验证制造协议，该团队将与国家可再生能源实验室的研究人员合作，制造和表征预计可在OPV太阳能电池中牢固组装成高效结构的成分。

项目成果

Simplified Models for Accelerated Structural Prediction of Conjugated Semiconducting Polymers

• DOI: 10.1021/acs.jpcc.7b09701
• 发表时间: 2017-11-30
• 期刊: JOURNAL OF PHYSICAL CHEMISTRY C
• 影响因子: 3.7
• 作者: Henry, Michael M.;Jones, Matthew L.;Jankowski, Eric
• 通讯作者: Jankowski, Eric

Perspective on coarse-graining, cognitive load, and materials simulation

• DOI: 10.1016/j.commatsci.2019.109129
• 发表时间: 2020-01
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Eric Jankowski;Neale Ellyson;Jenny W Fothergill;Michael M. Henry;Mitchell H. Leibowitz;Evan D Miller;Mone’t Alberts;Samantha Chesser;Jaime D. Guevara;Chris D. Jones;M. Klopfenstein;Kendra K. Noneman;Rachel Singleton;Ramon A. Uriarte-Mendoza;Stephen Thomas;Carla E. Estridge;Matthew L Jones

Machine learning predictions of electronic couplings for charge transport calculations of P3HT

用于 P3HT 电荷传输计算的电子耦合的机器学习预测

• DOI: 10.1002/aic.16760
• 发表时间: 2019
• 期刊: AIChE Journal
• 影响因子: 3.7
• 作者: Miller, Evan D.;Jones, Matthew L.;Henry, Mike M.;Stanfill, Bryan;Jankowski, Eric
• 通讯作者: Jankowski, Eric

Tying Together Multiscale Calculations for Charge Transport in P3HT: Structural Descriptors, Morphology, and Tie-Chains

• DOI: 10.3390/polym10121358
• 发表时间: 2018-11
• 期刊: Polymers
• 影响因子: 5
• 作者: Evan D Miller;Matthew L Jones;Eric Jankowski

Molecular Simulations for Understanding the Stabilization of Fullerenes in Water

了解富勒烯在水中稳定性的分子模拟

• DOI: 10.22369/issn.2153-4136/12/1/6
• 发表时间: 2021
• 期刊: The Journal of Computational Science Education
• 作者: Noneman, Kendra;Muhich, Christopher;Ausman, Kevin;Henry, Mike;Jankowski, Eric
• 通讯作者: Jankowski, Eric