Mesophase Engineering through Coarse-to-fine Grained Modeling

通过粗粒度到细粒度建模进行中间相工程

• 批准号: 2101829
• 负责人: Fernando Escobedo
• 金额: $ 32.98万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2101829&HistoricalAwards=false
• 关键词: Mesophase; Engineering; through; Coarse; fine

With support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Fernando Escobedo of Cornell University aims to create computational tools to engineer organic materials that organize into intricate structures at molecular scales. Simple physical models are very efficient in charting the generic properties and molecular organization of soft materials, but they lack the detail necessary to assign specific chemistries to the molecules, and to guide the materials synthesis to realize the sought-after properties. Escobedo will develop methods to allow the predictions of widely used physical models to be realizable by adding the missing chemical details. Escobedo will combine advanced molecular simulation methods and machine learning strategies to systematically identify chemistries most likely to fulfill the simple-model predictions of materials that form complex structures of interest. While the methods developed are expected to be applicable to many classes of organic materials, Escobedo will demonstrate their use with benchmark examples involving large multi-functional molecules capable of self-assembling into three-dimensional networks. Accordingly, results from this work could impact the advanced materials industry by guiding researchers to formulate stable composites for separation membranes and photovoltaics, and porous networks for catalysts and adsorbents. The project will enable the training of doctoral and undergraduate students in computational materials research. For outreach and education, the PI will coordinate a new workshop series organized at Cornell to celebrate the student accomplishments in research and in inclusivity.In this project, Dr. Escobedo is developing and applying molecular simulation strategies to identify polyphilic oligomers and functionalized nanoparticles capable of forming complex phases with partial structural order, called mesophases. In particular, starting from a computationally efficient but chemistry-agnostic (CA), coarse-grained (CG) model that forms a target mesophase, Escobedo will develop a scheme able to find chemistry-specific (CS) models which are fine-grained (FG) or atomistically-detailed that preserve the mesophase-formation ability. This approach will be applied to two distinct classes of CG models that have shown significant promise in generating complex mesophases: (I) Non-linear polyphilic oligomers whose distinct chemical blocks bring about nano-phase segregation, and (II) binary blends of nanoparticles exhibiting non-additive mixing behavior. In both cases, multiple complex 3D network phases have already been predicted and many others are potentially accessible. The mapping a given CA CG model into a CS FG model exhibiting the same sought-after mesophase behavior will entail the iterative use of a machine learning model to search through a predefined chemical space. Importantly, instead of directly searching for a CS FG model that maps into a target CA CG phase, Escobedo will use a selection “filter” at the fast, computationally efficient CG level. Specifically, the candidate chemistries will be first mapped into simple CS CG models to readily identify those able to form the target phase; these will then be mapped onto candidate CS FG models for further validation.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.

在化学系化学理论、模型和计算方法项目的支持下，康奈尔大学的费尔南多·埃斯科贝多 (Fernando Escobedo) 旨在创建计算工具来设计在分子尺度上组织成复杂结构的有机材料。软材料的通用特性和分子组织，但它们缺乏将特定化学成分分配给分子以及指导材料合成以实现受欢迎的特性所需的细节，Escobedo 将开发方法来进行广泛的预测。 Escobedo 将通过添加缺失的化学细节来使用物理模型来系统地识别最有可能实现形成感兴趣的复杂结构的材料的简单模型预测的化学物质。 Escobedo 预计适用于多种有机材料，将通过涉及能够自组装成三维网络的大型多功能分子的基准示例来展示其用途。因此，这项工作的结果可能会通过指导来影响先进材料行业。研究人员为分离膜和光伏发电以及催化剂和吸附剂的多孔网络配制稳定的复合材料，该项目将为计算材料研究方面的博士生和本科生提供培训，负责人将协调在康奈尔大学组织的一个新的研讨会系列。庆祝学生在研究和包容性方面取得的成就。在该项目中，Escobedo 博士正在开发和应用分子模拟策略来识别能够形成具有部分结构的复杂相的亲多性低聚物和功能化纳米粒子特别是，从形成目标中间相的计算效率高但化学不可知（CA）、粗粒度（CG）模型开始，Escobedo 将开发一种能够找到化学特异性（CS）模型的方案。具有细粒度 (FG) 或原子细节，可保留中间相形成能力。这种方法将应用于两类不同的 CG 模型，这些模型在生成复杂中间相方面显示出巨大的前景： (I) 非线性多亲低聚物，其独特的化学嵌段导致纳米相分离，以及 (II) 表现出非加性混合行为的纳米粒子二元混合物 在这两种情况下，已经预测了多个复杂的 3D 网络相和许多其他相。重要的是，将给定的 CA CG 模型映射到表现出相同的中间相行为的 CS FG 模型将需要迭代使用机器学习模型来搜索预定义的化学空间。在寻找映射到目标 CA CG 相的 CS FG 模型时，Escobedo 将在快速、计算高效的 CG 水平上使用选择“过滤器”。具体来说，候选化学物质将首先映射到简单的 CS CG 模型中，以便轻松识别这些化学物质。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Influence of Nonadditive Mixing on Colloidal Diamond Phase Formation from Patchy Particles

非加成混合对片状颗粒形成胶体金刚石相的影响

• DOI: 10.1021/acs.jpcb.3c00708
• 发表时间: 2023
• 期刊: The Journal of Physical Chemistry B
• 作者: Matos, Isabela Quintela;Escobedo, Fernando A.
• 通讯作者: Escobedo, Fernando A.

On the calculation of free energies over Hamiltonian and order parameters via perturbation and thermodynamic integration

通过微扰和热力学积分计算哈密顿量和有序参数的自由能

• DOI: 10.1063/5.0061541
• 发表时间: 2021
• 期刊: The Journal of Chemical Physics
• 作者: Escobedo, Fernando A.