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.

在化学理论，模型和计算方法计划中的支持下，康奈尔大学的费尔南多·埃斯科贝多（Fernando Escobedo）旨在为在分子尺度上组织为复杂的结构而创建计算工具。简单的物理模型在绘制软材料的通用性能和分子组织方面非常有效，但是它们缺乏将特定化学家分配给分子的必要细节，并且可以指导材料合成以实现感知性能。 Escobedo将开发方法，以通过添加缺失的化学细节来实现广泛使用物理模型的预测。 Escobedo将结合先进的分子模拟方法和机器学习策略，以系统地识别最有可能满足形成复杂结构的材料的简单模型预测的化学。虽然预计开发的方法适用于许多类别的有机材料，但Escobedo将证明它们与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使用。彼此之间，这项工作的结果可能会通过指导研究人员为分离机制和光伏的稳定组成以及用于催化剂和吸附剂的多孔网络来影响高级材料行业。该项目将使在计算材​​料研究中对博士和本科生进行培训。对于宣传和教育，PI将协调一个在康奈尔组织的新研讨会系列，以庆祝学生在研究和包容性方面的成就。在该项目中，Escobedo博士正在制定和应用分子模拟策略，以识别多物质寡聚物和功能化的纳米粒子，并能够形成具有部分结构性结构序列的复杂阶段，称为Messophases。特别是，从形成目标中间机的计算高效但化学不可能的（CA），粗粒（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，这些方案（FG）或原子化的模型（FG）或原子化模型可以保留介质化合物形成能力。这种方法将应用于两种不同类别的CG模型，这些模型在产生复杂的中间酶方面表现出了巨大的希望：（i）非线性多酚寡聚物，其独特的化学块带来了纳米相分离，以及（ii）表现出非辅助混合行为的纳米颗粒的二元混合物。在这两种情况下，都已经预测了多个复杂的3D网络阶段，并且许多其他阶段都可以访问。将给定的CA CG模型映射到表现出相同SCART的中间相行为的CS FG模型中，将需要迭代使用机器学习模型，以通过预定义的化学空间进行搜索。重要的是，Escobedo不是直接搜索将其映射到目标CA CG阶段的CS FG模型，而是将在快速，计算高效的CG级别上使用选择“滤波器”。具体而言，首先将候选化学物质映射到简单的CS CG模型中，以便轻松识别能够形成目标阶段的人。然后将这些映射到候选CS FG模型上以进行进一步验证。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估值得支持。

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.