CDS&E: A Machine Learning Architecture for General, Reusable Models for Guest-Host Chemical Bonding

CDS

• 批准号: 2154952
• 负责人: Matthew Montemore
• 金额: $ 42.85万
• 依托单位: Tulane University
• 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: 2022-05-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154952&HistoricalAwards=false
• 关键词: CDS&E; Machine; Learning; Architecture; General

Matthew M. Montemore of Tulane University is jointly funded by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, and the Established Program to Stimulate Competitive Research (EPSCoR) to develop a new, more efficient machine learning framework for chemistry and materials science. While machine-learning has been very useful for efficient screening of molecules and materials, previous approaches generally require a new machine learning model for each application. For example, a machine learning model that is used to design one type of battery can usually not be used for other types of batteries. Creating a new model from scratch for each battery type requires significant time and effort to generate data and perform fitting. In the work supported here, Dr. Montemore and his research group will develop the capability to generate machine learning models that can be reused for many applications; for example, a single model could be used for designing many types of batteries. Broadly, this will be useful in many areas of chemistry and materials science, and the Montemore group will release user-friendly code and models to allow other research groups to effectively leverage the framework. Dr. Montemore is also advising the local chapter of the Society for Hispanic Engineers, and working with Louisiana Dow Chemical to provide workshops and mentorship for the membership. Additionally, Dr. Montemore is advising a number of undergraduate researchers, including several from underrepresented groups. The new machine learning architecture that Dr. Montemore and his group will develop here is designed based on chemical principles, such as the existence of elements as discrete entities (and not as part of a continuous space). Briefly, the architecture uses latent (i.e., intermediate) variables to partially decouple different guests and different host elements, which greatly simplifies the learning task for each submodel. This is a significant departure from most screening approaches, which use off-the-shelf ML models to map continuous features onto a target variable and must learn chemical principles during fitting. The architecture is well-suited to handling heterogenous data sets, such as a mixture of computational and experimental data. It also does not require very large data sets, in contrast to deep-learning approaches. The models are often interpretable, as predictions can be explained in terms of latent variables or host features. Finally, the architecture is well-suited to transfer learning, which uses a pre-trained model to accelerate the creation of new models. In summary, this architecture harnesses fundamental chemical principles, especially those present in applications involving guest-host chemical bonding, to significantly increase the efficiency of materials screening. Overall, the primary benefits of this approach are significantly increased speedup in materials screening by allowing reusability, and the possibility of more sophisticated, effective screening by predicting multiple quantities.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.

马修·M杜兰大学的Montemore由化学系的化学理论，模型和计算方法项目以及刺激竞争研究的既定计划（EPSCoR）共同资助，为化学和材料科学开发一个新的，更有效的机器学习框架。虽然机器学习对于有效筛选分子和材料非常有用，但以前的方法通常需要为每个应用程序提供新的机器学习模型。例如，用于设计一种电池的机器学习模型通常不能用于其他类型的电池。为每种电池类型从头开始创建新模型需要大量的时间和精力来生成数据并执行拟合。在这里支持的工作中，Montemore博士和他的研究小组将开发生成机器学习模型的能力，这些模型可以在许多应用中重复使用;例如，单个模型可以用于设计多种类型的电池。从广义上讲，这在化学和材料科学的许多领域都很有用，蒙特莫尔小组将发布用户友好的代码和模型，以允许其他研究小组有效地利用该框架。蒙特莫尔博士还为西班牙裔工程师协会的当地分会提供咨询，并与路易斯安那州陶氏化学公司合作，为会员提供研讨会和指导。此外，蒙特莫尔博士还为一些本科生研究人员提供咨询，其中包括一些来自代表性不足的群体的研究人员。蒙特莫尔博士和他的团队将在这里开发的新机器学习架构是基于化学原理设计的，例如元素作为离散实体（而不是作为连续空间的一部分）存在。简而言之，该架构使用潜在的（即，中间）变量来部分解耦不同的客户和不同的主机元素，这大大简化了每个子模型的学习任务。这与大多数筛选方法有很大不同，后者使用现成的ML模型将连续特征映射到目标变量上，并且必须在拟合过程中学习化学原理。该架构非常适合处理异构数据集，例如计算和实验数据的混合。与深度学习方法相比，它也不需要非常大的数据集。这些模型通常是可解释的，因为预测可以用潜在变量或宿主特征来解释。最后，该架构非常适合迁移学习，它使用预先训练的模型来加速新模型的创建。总之，这种架构利用了基本的化学原理，特别是那些存在于涉及客体-主体化学键合的应用中的原理，以显著提高材料筛选的效率。总的来说，这种方法的主要好处是通过允许可重复使用性显著提高了材料筛选的速度，以及通过预测多个数量进行更复杂、更有效筛选的可能性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。