A Streamlit Based Dashboard for Green Chemistry Reaction Analysis

Abstract

Green chemistry aim is to reduce pollution, energy usage and environmental damage caused by chemical reactions through selection of sustainable processes. This work focuses on Ni (Nickel) catalyst based Suzuki-Miyaura cross-coupling reactions taken from Open Reaction Database (ORD). The system processes reaction data and calculates green chemistry metrics such as Atom Economy (AE), Reaction Mass Efficiency (RME), Environmental Factor (E-Factor), Energy Consumption and CO? Emission. These metrics are analyzed through statistical methods such as Confidence Interval (CI), pairwise catalyst comparison (p-test), Analysis of Variance (ANOVA), Cohen\'s d effect size, eta-squared and Pearson correlation to evaluate the statistical significance of catalyst selection on reaction yield. A rule based Recommendation System operates on observed reaction patterns. A Custom Reaction Evaluator is also include to compute metrics from user defined reaction conditions. The system is developed using Streamlit based interactive dashboard where users can filter reactions and compare different conditions using interactive graphs along with downloadable report in PDF, DOCX and CSV formats.

Introduction

This text presents a data-driven system for evaluating the environmental sustainability of chemical reactions using green chemistry principles. It highlights that traditional chemistry evaluation focuses mainly on product yield, ignoring waste generation, energy use, and environmental impact. To address this gap, the study integrates green chemistry metrics with structured reaction data from the Open Reaction Database (ORD) and automates sustainability analysis.

The work focuses on Ni-catalyzed Suzuki-Miyaura reactions, where nickel is considered a cheaper and potentially greener alternative to palladium, but still requires full sustainability evaluation rather than assumptions based only on cost. The system converts raw reaction data into standardized green metrics such as Atom Economy, Reaction Mass Efficiency, E-Factor, and Process Mass Intensity, enabling consistent comparison of reactions across conditions.

A Streamlit-based interactive dashboard is developed to allow users to explore reaction data visually, apply filters, analyze relationships between yield, waste, and efficiency, and generate downloadable reports in formats like PDF, DOCX, and CSV. This makes sustainability assessment more accessible and automated compared to manual calculations.

The literature review shows that while green chemistry metrics (like E-factor, PMI, and AE) and databases such as ORD exist, most previous work is either theoretical, manual, or limited to single-reaction analysis. Existing tools like AI4Green support metric calculation but lack large-scale dataset processing and comparative analysis capabilities. Statistical methods like ANOVA, correlation, and confidence intervals are also highlighted as important for evaluating differences across catalysts and reactions.

The proposed system is a modular pipeline consisting of data parsing, metric computation, statistical analysis, visualization, and report generation. It standardizes reaction data, applies uniform assumptions where needed, and ensures fair comparison across reactions. The analytical module performs statistical evaluation, while visualization tools and interactive filters help users interpret results effectively.

Conclusion

Green Chemistry Dashboard was built to make sustainability evaluation of chemical reactions simpler and more reliable than manual methods. The system takes real reaction data from ORD, computes green chemistry metrics, runs statistical analysis and presents everything through interactive visualizations in one platform. By combining all metrics into a single dashboard, users can compare catalyst performance and reaction conditions. Statistical methods further strengthen the analysis by confirming whether observed differences between catalysts are statistically real or just random variation. This work shows that data driven sustainability analysis gives much better insight into catalyst performance than just looking at yield alone. Features like Expert Recommendation System, Custom Reaction Evaluator and multi-format report generation make it useful for both documentation purposes. In short, the dashboard provides a simple and effective platform for sustainability analysis of chemical reactions. The system can be further improved by implement Machine Learning and larger datasets in future, but the current dashboard processed and visualized reaction data consistently without any major issues and all modules worked as expected.

References

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Copyright

Copyright © 2026 Manoj H N, Hemanth Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.