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EAGER: ADAPT: Hypotheses Generation in Heterogeneous Catalysis using Causal Inference and Machine Learning

EAGER：ADAPT：使用因果推理和机器学习在异质催化中生成假设

基本信息

批准号：
2231174
负责人：
BRYAN GOLDSMITH
金额：
$ 30万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2231174&HistoricalAwards=false
关键词：
EAGER ADAPT Hypotheses Generation Heterogeneous

项目摘要

With support from the Chemical Catalysis program in the Division of Chemistry (CHE), the Catalysis program from the Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET), and the Office of Multidisciplinary Activities (OMA), Bryan R. Goldsmith, Yixin Wang, and Suljo Linic of the University of Michigan, Ann Arbor will work to advance knowledge generation in heterogeneous catalysis using machine learning. They will develop the methods of interpretable machine learning and causal inference to generate hypotheses and extract insight of catalytic materials that are expected to lead to more predictive models of heterogeneous catalysts. This team’s research seeks to advance machine learning methods to find descriptors of catalytic performance (e.g., activity and selectivity) and, in this way, identify structure-property relationships that have the potential guide catalyst discovery efforts for important reactions pertaining to sustainability and energy applications. Their research addresses the National Science Foundation focus area of “AI for Concept Discovery”, and will benefit many areas beyond catalysis, such as enabling researchers to apply state-of-the-art machine learning algorithms to generate hypotheses and find new electrolytes or systems for renewable energy storage applications. This team will provide interdisciplinary training at the nexus of machine learning, statistics, and catalysis, which will help train an AI-aware workforce. They also will use a summer research internship program as a mechanism to broaden participation in AI-related STEM fields.Physically transparent models that can accurately quantify chemical and physical interactions between a surface of a material and adsorbate molecules (i.e., chemisorption) are crucial in many fields of chemistry and materials science. It has been known for a long time, going all the way back to the early 1900s, that chemisorption energies of adsorbates at gas/solid and liquid/solid interfaces are predictive descriptors of catalytic performance. There is a need to develop predictive theories of chemisorption that give insight into the underlying physical principles that govern chemical interaction at catalytic interfaces. Physically transparent and simple models that can accurately relate electronic and geometric features of a surface to its chemical properties and catalytic activity can allow us to rapidly predict or intuit which materials have specific chemical and catalytic features required for a particular application. This team will develop interpretable machine learning (i.e., models that can give researchers meaningful physical insights) and causal inference tools to generate hypotheses and extract insight that could lead to more predictive chemisorption models of heterogeneous catalysts. The team will focus on two state-of-the-art approaches; namely, generalized additive models and causal representation learning, and will advance these methods to understand adsorption of molecules on dilute alloy surfaces. A major goal is to identify causal links between electronic-structure, geometry, and chemisorption for dilute alloy catalysts. Although the focus here is on chemisorption and chemical catalysis on alloys, the developed methods are expected to be seamlessly integrated for use in other fields.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.

在化学、生物工程、环境和运输系统(CBET)和多学科活动办公室(OMA)化学、生物工程、环境和运输系统(CBET)部门的化学催化项目的支持下，密歇根大学安娜堡分校的Bryan R.Goldsmith、Wang和Suljo Linic将致力于利用机器学习促进多相催化领域的知识生成。他们将开发可解释的机器学习和因果推理的方法，以生成假设和提取对催化材料的洞察，这些材料有望导致更多关于多相催化剂的预测模型。该团队的研究旨在推进机器学习方法，以找到催化性能的描述符(例如，活性和选择性)，并通过这种方式确定具有潜在指导催化剂发现工作的结构-性质关系，用于与可持续性和能源应用有关的重要反应。他们的研究针对的是美国国家科学基金会重点关注的“人工智能概念发现”领域，并将惠及催化以外的许多领域，例如使研究人员能够应用最先进的机器学习算法来生成假设，并为可再生能源存储应用找到新的电解液或系统。该团队将在机器学习、统计和催化的结合点提供跨学科培训，这将有助于培训一支具有人工智能意识的劳动力。他们还将利用暑期研究实习计划作为一种机制，扩大对人工智能相关STEM领域的参与。物理透明模型可以准确量化材料表面和吸附分子之间的化学和物理相互作用(即化学吸附)，在许多化学和材料科学领域至关重要。早在20世纪初，人们就知道，吸附物在气/固和液/固界面上的化学吸附能是催化性能的预测指标。有必要发展化学吸附的预测理论，以深入了解支配催化界面化学相互作用的潜在物理原理。物理透明和简单的模型可以准确地将表面的电子和几何特征与其化学性质和催化活性联系起来，使我们能够快速预测或直观地了解哪些材料具有特定应用所需的特定化学和催化特征。该团队将开发可解释的机器学习(即，可以为研究人员提供有意义的物理见解的模型)和因果推理工具，以生成假设和提取洞察力，从而获得更具预测性的多相催化剂化学吸附模型。该团队将专注于两种最先进的方法，即广义加法模型和因果表示学习，并将推动这些方法来理解分子在稀薄合金表面的吸附。一个主要的目标是确定稀合金催化剂的电子结构、几何结构和化学吸附之间的因果联系。虽然这里的重点是对合金的化学吸附和化学催化，但开发的方法有望无缝整合，用于其他领域。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。