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

杜兰大学的Matthew M.Montemore是由化学系化学理论、模型和计算方法计划和已建立的刺激竞争研究计划(EPSCoR)共同资助的，旨在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种应用建立一个新的机器学习模型。例如，用于设计一种类型的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和进行拟合。在这里支持的工作中，蒙特莫尔博士和他的研究团队将开发生成机器学习模型的能力，这些模型可以重复用于许多应用；例如，一个模型可以用于设计多种类型的电池。总的来说，这将在化学和材料科学的许多领域有用，Montemore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。蒙特莫尔博士还为拉美裔工程师协会的当地分会提供建议，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，Montemore博士还为一些本科生研究人员提供建议，其中包括几名来自代表性不足群体的研究人员。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，比如元素作为离散实体(而不是作为连续空间的一部分)的存在。简而言之，该体系结构使用潜在(即中间)变量部分地分离不同的客户和不同的宿主元素，这极大地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的不同，后者使用现成的ML模型将连续的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。该体系结构非常适合处理异类数据集，例如计算数据和实验数据的混合。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，该体系结构非常适合迁移学习，它使用预先训练的模型来加快新模型的创建。总而言之，这种架构利用基本的化学原理，特别是那些存在于涉及客体-宿主化学键的应用中的原理，来显著提高材料筛选的效率。总体而言，这种方法的主要好处是通过允许重复使用显著提高材料筛选的速度，并通过预测多个数量来实现更复杂、更有效的筛选的可能性。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。

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.