{"id":6107,"date":"2025-07-28T13:42:18","date_gmt":"2025-07-28T04:42:18","guid":{"rendered":"https:\/\/aizoth.com\/?post_type=use_case&#038;p=6107"},"modified":"2026-04-06T11:40:10","modified_gmt":"2026-04-06T02:40:10","slug":"contents-e016","status":"publish","type":"use_case","link":"https:\/\/aizoth.com\/en\/use_case\/contents-e016\/","title":{"rendered":"Global Warming Potential Prediction Using Multi-Sigma\u00ae"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-outline is-style-outline--1\"><a class=\"wp-block-button__link has-black-color has-text-color has-background has-link-color has-medium-font-size has-custom-font-size wp-element-button\" href=\"https:\/\/aizoth.com\/en\/blog\/multi-sigma_2025_06_18\/\" style=\"background-color:#ffe200\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>More Details<\/strong><\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:60px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><strong><strong><strong>Refrigerants have global warming potentials (GWPs) thousands of times higher than CO\u2082, making them major climate contributors. As global policies like the Kigali Amendment push for low-GWP (&lt;100) alternatives, this study presents an AI-based framework on the Multi-Sigma<sup>\u00ae<\/sup> platform to predict GWP100 values of 207 refrigerants, enabling fast and efficient screening for sustainable refrigerant design.<\/strong><\/strong><\/strong><\/strong><\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:30px;text-decoration:underline\"><strong><strong><strong>1. AI analysis<\/strong><\/strong><\/strong><\/h2>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">The AI model was built using RDKit descriptors to featurize refrigerants listed in the IPCC AR6 report. Principal Component Analysis (PCA) reduced the dimensionality of input features, and quantile transformation was applied to normalize the skewed GWP distribution. An ensemble of three neural network models, trained on the Multi-Sigma<sup>\u00ae<\/sup> platform, achieved the highest accuracy, reaching an R\u00b2 score of 0.918 on the original GWP scale after inverse transformation.<\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"311\" src=\"https:\/\/aizoth.com\/wp-content\/uploads\/2025\/07\/E016_1-1024x311.png\" alt=\"AI analysis\nDeep learning framework for GWP prediction\nParity plot\n\" class=\"wp-image-6179\" srcset=\"https:\/\/aizoth.com\/wp-content\/uploads\/2025\/07\/E016_1-1024x311.png 1024w, https:\/\/aizoth.com\/wp-content\/uploads\/2025\/07\/E016_1-300x91.png 300w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:30px;text-decoration:underline\"><strong><strong><strong>2. Contribution Analysis<\/strong><\/strong><\/strong><\/h2>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">Using Multi-Sigma<sup>\u00ae<\/sup>\u2019s contribution analysis (explainable AI module) identified the most influential principal components (PCs) in GWP prediction: PC10, PC3, and PC4. PC10, associated with molecular weight, lipophilicity, and allylic oxides, showed a strong positive correlation with GWP. In contrast, PC3, linked to aliphatic heterocycles and topological indices, and PC4, related to nitriles and volume-based descriptors, contributed negatively to GWP. These insights highlight key molecular features that can guide the design of low-GWP refrigerants.<\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"323\" src=\"https:\/\/aizoth.com\/wp-content\/uploads\/2025\/07\/E016_4-1024x323.png\" alt=\"\" class=\"wp-image-7387\" srcset=\"https:\/\/aizoth.com\/wp-content\/uploads\/2025\/07\/E016_4-1024x323.png 1024w, https:\/\/aizoth.com\/wp-content\/uploads\/2025\/07\/E016_4-300x95.png 300w, https:\/\/aizoth.com\/wp-content\/uploads\/2025\/07\/E016_4.png 2014w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><strong>The developed GWP prediction model can be applied to screen chemical databases for refrigerant candidates that exhibit low GWP and align with key molecular features identified through contribution analysis.<\/strong><\/strong><\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-small-font-size wp-block-paragraph\">This usecase is based on results obtained from a project, JPNP23001, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).<\/p>\n\n\n\n<p class=\"has-small-font-size wp-block-paragraph\">Note: The data used in this analysis was processed and edited based on the data published in the article below, under Creative Commons Zero 1.0 Universal (CC0 1.0) license.<br>Data Source: Rajapriya Navin and Kotaro Kawajiri. &#8220;A Deep Learning Framework for Predicting Global Warming Potential of Refrigerants for Sustainable Chemical Design&#8221;ACS Sustainable Chem. Eng. 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