Collaborative Research: DMREF: Computationally Driven Discovery and Synthesis of 2D Materials through Selective Etching

合作研究：DMREF：通过选择性蚀刻计算驱动的 2D 材料发现和合成

• 批准号: 2324156
• 负责人: Konstantin Klyukin
• 金额: $ 121.92万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324156&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Computationally; Driven

Non-technical Description: The discovery of novel two-dimensional (2D) materials is a very attractive research direction that can have a profound impact on a variety of applications in energy, environmental science, catalysis, and electronics. The selective electrochemical extraction of atoms from layered 3D structures promises a significant expansion of the world of 2D materials beyond the widely explored layered structures with weak interlayer interactions. To advance a new process for the synthesis of 2D materials and accelerate their discovery, a multidisciplinary team of chemists, physicists, and materials scientists will combine state-of-the-art computational, experimental, and machine-learning techniques in a closed-loop paradigm inspired by the Materials Genome Initiative. The project also includes high school outreach programs and interdisciplinary training of the next-generation workforce to cover a broad spectrum from synthesis and characterization to theory and modeling of materials.Technical Description: This DMREF project will provide the enabling fundamental knowledge required to develop a very selective electrochemical removal process for the synthesis of novel 2D materials of various compositions and structures. Initially focusing on a family of 2D transition metal carbides and nitrides (MXenes), our team will design a computational discovery methodology with experimental validation and closed-loop optimization of predictive models, focusing on the identification of promising 3D precursor phases and relevant etching conditions. The developed toolset will allow us to evaluate if the extraction of atomic layers is possible under electrochemical conditions, whether electrochemical etching will be selective and is not limited by kinetics, and whether the desired 2D structure will be stable in contact with electrolyte. Establishing relations of physics-based descriptors to experimental results and outcomes of simulations will allow us to carry out screening of large combinatorial material space. To validate the computational predictions, our team will perform high-throughput electrochemical etching of materials and low-throughput detailed materials characterization.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.

非技术描述：新型二维（2D）材料的发现是一个非常有吸引力的研究方向，可以对能源，环境科学，催化和电子学的各种应用产生深远的影响。从分层3D结构中的原子的选择性电化学提取有望在2D材料的世界中显着扩展，超出了广泛探索的分层结构，并具有弱层间相互作用。为了推进合成2D材料并加速其发现的新过程，一个化学家，物理学家和材料科学家组成的多学科团队将将最新的计算，实验性和机器学习技术结合在受材料基因组启动启发的封闭环范式中。该项目还包括高中宣传计划和下一代劳动力的跨学科培训，以涵盖从合成和特征到理论到材料建模的广泛范围。技术描述：该DMREF项目将为开发出非常选择性的基本知识，以开发出非常有选择性的电化学重新处理过程，以构成各种新型构图和各种构图和结构的合成。最初着重于2D过渡金属碳化物和硝酸盐（MXENES）的家族，我们的团队将设计一种计算发现方法，并通过实验验证和预测模型的闭环优化，重点是鉴定有希望的3D前体相和相关的蚀刻条件。开发的工具集将使我们能够在电化学条件下评估原子层是否可以提取原子层，是否可以选择性蚀刻，并且不受动力学的限制，以及所需的2D结构是否会稳定在与电解质接触中。建立基于物理学的描述符与实验结果和模拟结果的关系将使我们能够对大型组合物质空间进行筛查。为了验证计算预测，我们的团队将对材料和低通量的详细材料表征进行高通量电化学蚀刻。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准通过评估来进行评估的。