CAREER: Multiscale and Machine Learning Approaches for Electrified Interfaces

北德克萨斯大学的Oliviero Andreussi博士获得了化学系化学理论、模型和计算方法项目以及材料研究部凝聚态物质和材料理论（CMMT）项目的奖励。他将开发和应用新的计算工具来表征固液界面的化学过程。该项目结合了分层模型和机器学习技术，为控制化学设备（如电池、燃料电池和传感设备）的操作提供准确而廉价的描述。开发的技术旨在从新兴的二维材料开始，对电催化材料进行系统的虚拟筛选。发展计算思维来解决新出现的技术问题代表了该项目的关键教育组成部分。教育部分扩展了计算的使用，使科学可视化，并使其易于获取和吸引公众。黑客马拉松研讨会将被采用，以吸引年轻的研究人员参与计算思维。该团队还将使用和开发可视化工具，以扩大研究对其他领域和学科的影响。Oliviero Andreussi正在开发精确和可转移的方法来模拟固液界面。为了实现这一目标，该项目以综合研究和教育计划为特色，重点是通过嵌入材料的第一性原理描述来扩展电化学环境的连续体模型。Andreussi博士和他的研究小组正在寻求环境影响的混合多尺度方法和机器学习策略的新发展。该研究提高了湿界面和带电界面模拟的可移植性和准确性。这些新方法和技术被应用于研究复杂嵌入环境对新兴二维（2D）材料的影响。开发的计算工具允许系统筛选现有和拟议的2D材料，以探索剥离策略，验证其在复杂环境中的稳定性，表征其（电）催化活性，并确定其在传感设备中的作用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Dr. Oliviero Andreussi of the University of North Texas is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry and from the Condensed Matter and Materials Theory (CMMT) program in the Division of Materials Research. He will develop and apply new computational tools to the characterization of chemical processes at solid-liquid interfaces. The project combines hierarchical models and machine-learning techniques to provide accurate and inexpensive descriptions of aspects that control the operation of chemical devices, such as batteries, fuel cells, and sensing devices. The developed techniques are aimed at the systematic virtual screening of materials for electrocatalysis, starting from the emerging class of two-dimensional materials. The development of a computational mindset to address emerging technological problems represents the key educational component of the project. The educational component extends the use of computation to visualize science and to make it accessible and attractive to the public. Hackathon workshops will be adopted to engage younger researchers in computational thinking. The team will also use and develop visualization tools to expand the impact of the research to other fields and disciplines.Dr. Oliviero Andreussi is developing accurate and transferable approaches for modeling solid-liquid interfaces. To accomplish this goal, this project features an integrated research and education program focused on extending continuum models of electrochemical environments by embedding a first-principles description of materials. Dr Andreussi and his research group are pursuing new developments in hybrid multiscale approaches and machine-learning strategies of environment effects. The research improves the transferability and accuracy of simulations of wet and electrified interfaces. These new methods and techniques are applied to study the effects of complex embedding environments on the emerging class of two-dimensional (2D) materials. The developed computational tools allow a systematic screening of existing and proposed 2D materials to explore exfoliation strategies, to verify their stability in complex environments, to characterize their (electro-)catalytic activities, and to identify their role in sensing devices.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.