NJmat: Data-Driven Machine Learning Interface to Accelerate Material Design,Journal of Chemical Information and Modeling

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NJmat: Data-Driven Machine Learning Interface to Accelerate Material Design
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2024-08-12 , DOI: 10.1021/acs.jcim.4c00493
Yiru Huang ₁ , Lei Zhang ₁ , Hangyuan Deng ₁ , Junfei Mao ₁

Affiliation

Machine learning techniques have significantly transformed the way materials scientists conduct research. However, the widespread deployment of machine learning software in daily experimental and simulation research for materials and chemical design has been limited. This is partly due to the substantial time investment and learning curve associated with mastering the necessary codes and computational environments. In this paper, we introduce a user-friendly, data-driven machine learning interface featuring multiple “button-clicking” functionalities to streamline the design of materials and chemicals. This interface automates the processes of transforming materials and molecules, performing feature selection, constructing machine learning models, making virtual predictions, and visualizing results. Such automation accelerates materials prediction and analysis in the inverse design process, aligning with the time criteria outlined by the Materials Genome Initiative. With simple button clicks, researchers can build machine learning models and predict new materials once they have gathered experimental or simulation data. Beyond the ease of use, NJmat offers three additional features for data-driven materials design: (1) automatic feature generation for both inorganic materials (from chemical formulas) and organic molecules (from SMILES), (2) automatic generation of Shapley plots, and (3) automatic construction of “white-box” genetic models and decision trees to provide scientific insights. We present case studies on surface design for halide perovskite materials encompassing both inorganic and organic species. These case studies illustrate general machine learning models for virtual predictions as well as the automatic featurization and Shapley/genetic model construction capabilities. We anticipate that this software tool will expedite materials and molecular design within the scope of the Materials Genome Initiative, particularly benefiting experimentalists.

中文翻译：

NJmat：数据驱动的机器学习接口，加速材料设计

机器学习技术极大地改变了材料科学家进行研究的方式。然而，机器学习软件在材料和化学设计的日常实验和模拟研究中的广泛部署受到限制。这部分是由于与掌握必要的代码和计算环境相关的大量时间投入和学习曲线。在本文中，我们介绍了一种用户友好的、数据驱动的机器学习界面，具有多个“按钮点击”功能，可简化材料和化学品的设计。该界面自动执行材料和分子转换、执行特征选择、构建机器学习模型、进行虚拟预测和可视化结果的过程。这种自动化加速了逆向设计过程中的材料预测和分析，与材料基因组计划概述的时间标准保持一致。只需点击简单的按钮，研究人员就可以构建机器学习模型，并在收集实验或模拟数据后预测新材料。除了易于使用之外，NJmat 还为数据驱动的材料设计提供了三个附加功能：(1) 自动生成无机材料（来自化学式）和有机分子（来自 SMILES）的特征，(2) 自动生成 Shapley 图，（3）自动构建“白盒”遗传模型和决策树以提供科学见解。我们介绍了涵盖无机和有机物种的卤化物钙钛矿材料的表面设计案例研究。这些案例研究说明了用于虚拟预测的通用机器学习模型以及自动特征化和 Shapley/遗传模型构建功能。我们预计该软件工具将加快材料基因组计划范围内的材料和分子设计，特别有利于实验人员。

更新日期：2024-08-12

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