CAREER: Multiscale and Machine Learning Approaches for Electrified Interfaces

职业：电气化接口的多尺度和机器学习方法

基本信息

批准号：
1945139
负责人：
Oliviero Andreussi
金额：
$ 60万
依托单位：
University of North Texas
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1945139&HistoricalAwards=false
关键词：
CAREER Multiscale Machine Learning Approaches

项目摘要

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.

北德克萨斯大学的Oliviero Andreussi博士得到了化学理论，模型和计算方法的奖项，并在材料研究部中的化学理论，模型和计算方法计划以及凝结的物质和材料理论（CMMT）计划（CMMT）计划的支持。他将开发并将新的计算工具应用于固定液体界面上化学过程的表征。该项目结合了分层模型和机器学习技术，以提供控制化学设备（例如电池，燃料电池和传感设备）操作的方面的准确且廉价的描述。开发的技术旨在从新兴类别的二维材料等级开始对电催化的材料进行系统的虚拟筛选。解决新兴技术问题的计算心态的发展代表了项目的关键教育组成部分。教育组成部分扩展了使用计算来可视化科学并使其对公众的访问和吸引力。黑客马拉松研讨会将被通过以吸引年轻的研究人员参与计算思维。团队还将使用和开发可视化工具将研究的影响扩展到其他领域和学科。 Oliviero Andreussi正在开发准确且可转移的方法来建模固定液体界面。为了实现这一目标，该项目采用集成的研究和教育计划，旨在通过嵌入材料的第一原理描述来扩展电化学环境的连续模型。 Andreussi博士及其研究小组正在追求混合多尺度方法和机器学习策略的新发展。该研究提高了湿式和电气界面模拟的可传递性和准确性。这些新方法和技术用于研究复杂嵌入环境对二维（2D）材料新兴类别的影响。 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.