EAGER: SSMCDAT2023: Deep learning Gibbs free energy functions to guide solid-state material synthesis

EAGER：SSMCDAT2023：深度学习吉布斯自由能函数指导固态材料合成

• 批准号: 2334275
• 负责人: Yong-Jie Hu
• 金额: $ 19.85万
• 依托单位: Drexel 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: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334275&HistoricalAwards=false
• 关键词: EAGER; SSMCDAT2023; Deep; learning; Gibbs

NONTECHNICAL SUMMARY:This award is made on an EAGER proposal. It supports progress on a project advanced at the SSMCDAT 2023 Datathon held at Lehigh University. This EAGER award supports research aimed at advancing our understanding of how to synthesize solid inorganic materials, which serve as the cornerstone for novel functional devices to meet societal needs for clean energy, environmental sustainability, and human welfare. Nowadays, many new inorganic materials have been virtually designed using computers, but they have rarely been converted into real applications because of missing knowledge of how to synthesize them in practice. In this project, a team of interdisciplinary researchers with expertise in materials science and computer science will develop a data-driven framework to predict the best ways to synthesize these computationally designed materials. The team will utilize deep learning algorithms of artificial intelligence to efficiently predict the free energies of the material to be synthesized and their possible precursors, considering the effects of temperature, pressure, and chemical compositions of the materials. With these predictions, the team will assess the feasibility of the chemical reactions associated with synthesis and correspondingly identify the optimal reaction conditions. To validate and improve their computational predictions, the researchers will also conduct laboratory experiments where they will closely monitor the synthesis process as it happens using a real time characterization technique. To demonstrate the effectiveness of their approach, the researchers will focus on successfully synthesizing a group of novel inorganic compounds known as transition metal oxynitrides, which are promising for energy conversion applications but not yet fully realized experimentally. Through this project, the team aims to bridge the gap between computational design and practical synthesis, unlocking new possibilities for novel functional materials that can benefit society.This award also supports activities to make the field of science and engineering more inclusive and diverse. It will broaden the participation of women and young girls in these fields, especially in countries in eastern Africa. It will also promote research involvement among undergraduate students from underrepresented backgrounds. Moreover, the researchers will develop new educational materials to teach data-driven materials science to undergraduate students at Drexel University. TECHNICAL SUMMARY:This award is made on an EAGER proposal. It supports progress on a project advanced at the SSMCDAT 2023 Datathon held at Lehigh University. This EAGER award supports research aimed at advancing scientific knowledge in the synthesizability of solid-state inorganic materials, especially those that have been computationally designed but not yet realized experimentally. The research will develop a data-driven framework, integrating deep learning algorithms, computational thermodynamic modeling, and validation experiments to efficiently predict synthesis pathways and optimal conditions. Utilizing deep learning algorithms, the framework will predict temperature-dependent Gibbs free energy functions for various compound stoichiometries, based on the relationship between thermochemical properties of inorganic compounds and their electronic structures. With the predictions of Gibbs free energy functions for the compound to be synthesized and its various possible precursors, the framework will then model the thermodynamics of each candidate synthesis reaction in the context of the CALculation of PHAse Diagram (CALPHAD) method. Suitable precursors and synthesis conditions will be rationally identified by assessing the dependence of reaction spontaneity on the key controlling parameters of practical synthesis. Experimental understanding of the reaction pathway and its dependence on reaction conditions obtained through an in situ synthesis approach will be used to validate and improve modeling predictions. The team will focus on successfully synthesizing a group of computationally designed transition metal oxynitrides, highly interesting for energy conversion applications but not yet fully realized experimentally, as a demonstration of the framework's capabilities. Through this project, the team aims to bridge the gap between computational design and practical synthesis, unlocking new possibilities for novel functional materials that can benefit society.This award also supports various education and outreach activities that will 1) expand the involvement of women and young girls in the fields of science and engineering, particularly in countries in eastern Africa; 2) promote research participation among underrepresented undergraduate students; and 3) develop new learning modules that incorporate data-driven materials science in the undergraduate curriculum at Drexel University.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.

非技术摘要：该奖项是根据渴望的提议颁发的。它支持在Lehigh University在Lehigh University举行的SSMCDAT 2023 DATATHON上高级项目的进展。这项渴望的奖项支持旨在促进我们对如何综合固体无机材料的理解的研究，这些材料是满足清洁能源，环境可持续性和人类福利的社会需求的新型功能设备的基石。如今，许多新的无机材料实际上是使用计算机设计的，但是由于缺少如何在实践中合成它们的知识，因此很少将它们转换为真实的应用程序。在这个项目中，一组具有材料科学和计算机科学专业知识的跨学科研究人员将开发一个数据驱动的框架，以预测合成这些计算设计材料的最佳方法。考虑到温度，压力和化学成分的影响，团队将利用人工智能的深度学习算法有效预测要合成的材料的自由能及其可能的前体。通过这些预测，团队将评估与合成相关的化学反应的可行性，并相应地确定最佳反应条件。为了验证和改善其计算预测，研究人员还将进行实验室实验，在其中使用实时表征技术进行合成过程，以密切监测合成过程。为了证明其方法的有效性，研究人员将专注于成功合成一组新型的无机化合物，称为过渡金属氧气，这对于能量转换应用是有希望的，但尚未在实验中完全实现。通过该项目，该团队旨在弥合计算设计与实用综合之间的差距，从而解开了可以使社会受益的新型功能材料的新可能性。该奖项还支持使科学和工程领域的活动更具包容性和多样性。它将扩大妇女和年轻女孩在这些领域的参与，尤其是在东非的国家。它还将促进来自代表性不足背景的本科生的研究参与。此外，研究人员将开发新的教育材料，以向Drexel University的本科生传授数据驱动的材料科学。技术摘要：该奖项是根据急切的建议颁发的。它支持在Lehigh University在Lehigh University举行的SSMCDAT 2023 DATATHON上高级项目的进展。这项渴望的奖项支持旨在提高固态无机材料合成性的科学知识的研究，尤其是那些经过计算设计但尚未实验实现的材料。该研究将开发一个数据驱动的框架，整合深度学习算法，计算热力学建模和验证实验，以有效预测合成途径和最佳条件。利用深度学习算法，该框架将根据无机化合物的热化学性质及其电子结构之间的关系，预测各种化合物化学对象的温度依赖性吉布斯自由能函数。通过对吉布斯自由能函数的预测进行合成的化合物及其各种可能的前体，该框架将在计算相位图（Calphad）方法的背景下对每个候选综合反应的热力学进行建模。通过评估反应自发性对实际合成的关键控制参数的依赖性，合理的前体和合成条件将在理性上识别。对反应途径及其对通过原位合成方法获得的反应条件的依赖性的实验理解将用于验证和改善建模预测。该团队将专注于成功综合一组计算设计的过渡金属氧气，对于能量转换应用而言非常有趣，但尚未在实验上完全实现，以证明该框架的能力。通过该项目，该团队旨在弥合计算设计与实践综合之间的差距，从而为可以使社会受益的新型功能材料解锁了新的可能性。该奖项还支持各种教育和外展活动，这些活动将会有1）扩大妇女和年轻女孩在科学和工程领域的参与，尤其是在东非国家 /地区的国家； 2）促进人数不足的本科生的研究参与； 3）开发新的学习模块，这些模块将数据驱动的材料科学纳入了德雷克塞尔大学的本科课程。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和更广泛影响的评估审查标准来通过评估来获得支持的。