Predicting the HOMO-LUMO gap of benzenoid polycyclic hydrocarbons via interpretable machine learning

Zheng, Fengru; Zhu, Zhang; Lu, Junlin; Yan, Yuyi; Jiang, Hao; Sun, Qiang

doi:10.1016/j.cplett.2023.140358

Cited by 9 publications

(5 citation statements)

References 43 publications

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“…Most machine learning models developed for HOMO–LUMO gaps have been those for organic structures. For example, Zheng et al have previously created machine learning models for benzenoid polycyclic hydrocarbons. − Also for organic compounds, von Lilienfeld and co-workers developed models with prediction errors of ∼0.1 eV and showcased methods for achieving improved data efficiency, which is often problematic in HOMO–LUMO gap models. Zhang and Aires-de-Sousa created a B3LYP structure and HOMO and LUMO energy database for tens of thousands of organic structures and then trained a variety of machine learning models and found accuracy up to 0.15 eV.…”

Section: Results and Discussionmentioning

confidence: 99%

Machine Learning Models for Predicting Zirconocene Properties and Barriers

Kirkland,

Kumawat,

Shaban Tameh

et al. 2024

J. Chem. Inf. Model.

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Zr metallocenes have significant potential to be highly tunable polyethylene catalysts through modification of the aromatic ligand framework. Here we report the development of multiple machine learning models using a large library (>700 systems) of DFT-calculated zirconocene properties and barriers for ethylene polymerization. We show that very accurate machine learning models are possible for HOMO–LUMO gaps of precatalysts but the performance significantly depends on the machine learning algorithm and type of featurization, such as fingerprints, Coulomb matrices, smooth overlap of atomic positions, or persistence images. Surprisingly, the description of the bonding hapticity, the number of direct connections between Zr and the ligand aromatic carbons, only has a moderate influence on the performance of most models. Despite robust models for HOMO–LUMO gaps, these types of machine learning models based on structure connectivity type features perform poorly in predicting ethylene migratory insertion barrier heights. Therefore, we developed several relatively robust and accurate machine learning models for barrier heights that are based on quantum-chemical descriptors (QCDs). The quantitative accuracy of these models depends on which potential energy surface structure QCDs were harvested from. This revealed a Hammett-type principle to naturally emerge showing that QCDs from the π-coordination complexes provide much better descriptions of the transition states than other potential-energy structures. Feature importance analysis of the QCDs provides several fundamental principles that influence zirconocene catalyst reactivity.

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Section: Results and Discussionmentioning

confidence: 99%

Machine Learning Models for Predicting Zirconocene Properties and Barriers

Kirkland,

Kumawat,

Shaban Tameh

et al. 2024

J. Chem. Inf. Model.

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“…In particular, unsupervised learning techniques such as t-distributed stochastic neighbor embedding and principal component analysis excel in extracting meaningful descriptors through data-driven insights [118,134] (Figure 2). These MLidentified descriptors, in turn, serve as valuable, interpretable inputs for predictive ML models to predict various material properties across different discipline [135,136] For instance, these models successfully predicted HOMO-LUMO gaps [137] and identified descriptors associated with volcano plots, [138,139] reactivity, [114,[140][141][142][143] and electronic properties of transition metal complexes [144] in the field of homogeneous catalysis. In solid-state chemistry, these models have been leveraged to study the material properties of crystals, [145,146] construct phase diagrams, [147] assess the redox potentials of Li-ion batteries, [148] and evaluate thermodynamic stability.…”

Section: Descriptor-based and Interpretable ML Modelsmentioning

confidence: 99%

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Aldossary,

Campos‐Gonzalez‐Angulo,

Pablo‐García

et al. 2024

Advanced Materials

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Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

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“…Recently, machine learning techniques, particularly computer vision, have emerged as promising tools in material science research for automating the analysis and processing of image data [18][19][20][21][22][23][24][25]. Machine learning has achieved remarkable advancements even in the field of medical and biological sciences [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Applying a Deep-Learning-Based Keypoint Detection in Analyzing Surface Nanostructures

Yuan

Zhu

et al. 2023

Molecules

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Scanning tunneling microscopy (STM) imaging has been routinely applied in studying surface nanostructures owing to its capability of acquiring high-resolution molecule-level images of surface nanostructures. However, the image analysis still heavily relies on manual analysis, which is often laborious and lacks uniform criteria. Recently, machine learning has emerged as a powerful tool in material science research for the automatic analysis and processing of image data. In this paper, we propose a method for analyzing molecular STM images using computer vision techniques. We develop a lightweight deep learning framework based on the YOLO algorithm by labeling molecules with its keypoints. Our framework achieves high efficiency while maintaining accuracy, enabling the recognitions of molecules and further statistical analysis. In addition, the usefulness of this model is exemplified by exploring the length of polyphenylene chains fabricated from on-surface synthesis. We foresee that computer vision methods will be frequently used in analyzing image data in the field of surface chemistry.

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Predicting the HOMO-LUMO gap of benzenoid polycyclic hydrocarbons via interpretable machine learning

Cited by 9 publications

References 43 publications

Machine Learning Models for Predicting Zirconocene Properties and Barriers

Machine Learning Models for Predicting Zirconocene Properties and Barriers

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Applying a Deep-Learning-Based Keypoint Detection in Analyzing Surface Nanostructures

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