Jaehyun Park, Farshud Sorourifar, Madhav R. Muthyala, Abigail M. Houser, Madison Tuttle, Joel A. Paulson* and Shiyu Zhang*,
{"title":"利用机器学习零距离发现高性能、低成本有机电池材料","authors":"Jaehyun Park, Farshud Sorourifar, Madhav R. Muthyala, Abigail M. Houser, Madison Tuttle, Joel A. Paulson* and Shiyu Zhang*, ","doi":"10.1021/jacs.4c1166310.1021/jacs.4c11663","DOIUrl":null,"url":null,"abstract":"<p >Organic electrode materials (OEMs), composed of abundant elements such as carbon, nitrogen, and oxygen, offer sustainable alternatives to conventional electrode materials that depend on finite metal resources. The vast structural diversity of organic compounds provides a virtually unlimited design space; however, exploring this space through Edisonian trial-and-error approaches is costly and time-consuming. In this work, we develop a new framework, SPARKLE, that combines computational chemistry, molecular generation, and machine learning to achieve zero-shot predictions of OEMs that simultaneously balance reward (specific energy), risk (solubility), and cost (synthesizability). We demonstrate that SPARKLE significantly outperforms alternative black-box machine learning algorithms on interpolation and extrapolation tasks. By deploying SPARKLE over a design space of more than 670,000 organic compounds, we identified ≈5000 novel OEM candidates. Twenty-seven of them were synthesized and fabricated into coin-cell batteries for experimental testing. Among SPARKLE-discovered OEMs, 62.9% exceeded benchmark performance metrics, representing a 3-fold improvement over OEMs selected by human intuition alone (20.8% based on six years of prior lab experience). The top-performing OEMs among the 27 candidates exhibit specific energy and cycling stability that surpass the state-of-the-art while being synthesizable at a fraction of the cost.</p>","PeriodicalId":49,"journal":{"name":"Journal of the American Chemical Society","volume":"146 45","pages":"31230–31239 31230–31239"},"PeriodicalIF":14.4000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning\",\"authors\":\"Jaehyun Park, Farshud Sorourifar, Madhav R. Muthyala, Abigail M. Houser, Madison Tuttle, Joel A. Paulson* and Shiyu Zhang*, \",\"doi\":\"10.1021/jacs.4c1166310.1021/jacs.4c11663\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >Organic electrode materials (OEMs), composed of abundant elements such as carbon, nitrogen, and oxygen, offer sustainable alternatives to conventional electrode materials that depend on finite metal resources. The vast structural diversity of organic compounds provides a virtually unlimited design space; however, exploring this space through Edisonian trial-and-error approaches is costly and time-consuming. In this work, we develop a new framework, SPARKLE, that combines computational chemistry, molecular generation, and machine learning to achieve zero-shot predictions of OEMs that simultaneously balance reward (specific energy), risk (solubility), and cost (synthesizability). We demonstrate that SPARKLE significantly outperforms alternative black-box machine learning algorithms on interpolation and extrapolation tasks. By deploying SPARKLE over a design space of more than 670,000 organic compounds, we identified ≈5000 novel OEM candidates. Twenty-seven of them were synthesized and fabricated into coin-cell batteries for experimental testing. Among SPARKLE-discovered OEMs, 62.9% exceeded benchmark performance metrics, representing a 3-fold improvement over OEMs selected by human intuition alone (20.8% based on six years of prior lab experience). The top-performing OEMs among the 27 candidates exhibit specific energy and cycling stability that surpass the state-of-the-art while being synthesizable at a fraction of the cost.</p>\",\"PeriodicalId\":49,\"journal\":{\"name\":\"Journal of the American Chemical Society\",\"volume\":\"146 45\",\"pages\":\"31230–31239 31230–31239\"},\"PeriodicalIF\":14.4000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the American Chemical Society\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/jacs.4c11663\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Chemical Society","FirstCategoryId":"92","ListUrlMain":"https://pubs.acs.org/doi/10.1021/jacs.4c11663","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning
Organic electrode materials (OEMs), composed of abundant elements such as carbon, nitrogen, and oxygen, offer sustainable alternatives to conventional electrode materials that depend on finite metal resources. The vast structural diversity of organic compounds provides a virtually unlimited design space; however, exploring this space through Edisonian trial-and-error approaches is costly and time-consuming. In this work, we develop a new framework, SPARKLE, that combines computational chemistry, molecular generation, and machine learning to achieve zero-shot predictions of OEMs that simultaneously balance reward (specific energy), risk (solubility), and cost (synthesizability). We demonstrate that SPARKLE significantly outperforms alternative black-box machine learning algorithms on interpolation and extrapolation tasks. By deploying SPARKLE over a design space of more than 670,000 organic compounds, we identified ≈5000 novel OEM candidates. Twenty-seven of them were synthesized and fabricated into coin-cell batteries for experimental testing. Among SPARKLE-discovered OEMs, 62.9% exceeded benchmark performance metrics, representing a 3-fold improvement over OEMs selected by human intuition alone (20.8% based on six years of prior lab experience). The top-performing OEMs among the 27 candidates exhibit specific energy and cycling stability that surpass the state-of-the-art while being synthesizable at a fraction of the cost.
期刊介绍:
The flagship journal of the American Chemical Society, known as the Journal of the American Chemical Society (JACS), has been a prestigious publication since its establishment in 1879. It holds a preeminent position in the field of chemistry and related interdisciplinary sciences. JACS is committed to disseminating cutting-edge research papers, covering a wide range of topics, and encompasses approximately 19,000 pages of Articles, Communications, and Perspectives annually. With a weekly publication frequency, JACS plays a vital role in advancing the field of chemistry by providing essential research.