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

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、催化和电子等领域的各种应用产生深远的影响。从层状3D结构中选择性地电化学提取原子有望使2D材料的世界大大扩展到广泛探索的具有弱层间相互作用的层状结构之外。为了推进2D材料合成的新过程并加速其发现，一个由化学家、物理学家和材料科学家组成的多学科团队将在材料基因组倡议的启发下，将最先进的计算、实验和机器学习技术结合在一个闭环范式中。该项目还包括高中外展计划和对下一代劳动力的跨学科培训，涵盖从材料的合成和表征到材料的理论和模型的广泛范围。技术描述：该DMREF项目将提供必要的基础知识，以开发一种非常选择性的电化学去除工艺，用于合成各种成分和结构的新型2D材料。我们的团队最初专注于一系列2D过渡金属碳化物和氮化物(MXenes)，将设计一种计算发现方法，通过实验验证和预测模型的闭环优化，专注于识别有希望的3D前驱物相和相关的刻蚀条件。开发的工具包将允许我们评估在电化学条件下提取原子层是否可能，电化学蚀刻是否将是选择性的且不受动力学限制，以及所需的2D结构是否将在与电解液接触时保持稳定。建立基于物理的描述子与实验结果和模拟结果的关系将使我们能够进行大型组合材料空间的筛选。为了验证计算预测，我们的团队将对材料进行高通量电化学蚀刻和低通量详细材料表征。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 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.