{"title":"演绎式机器学习在化学应用中的挑战与机遇","authors":"Tianfan Jin, Brett M. Savoie","doi":"10.1146/annurev-chembioeng-100722-111917","DOIUrl":null,"url":null,"abstract":"Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive, reasoning is required and still depends on manual intervention by an expert. This review describes the characteristics of deductive reasoning that are helpful for understanding the role played by expert intervention in problem-solving and explains why such interventions are often relatively resistant to disruption by typical machine learning strategies. The article then discusses the factors that contribute to creating a deductive bottleneck, how deductive bottlenecks are currently addressed in several application areas, and how machine learning models capable of deduction can be designed. The review concludes with a tutorial case study that illustrates the challenges of deduction problems and a notebook for readers to experiment with on their own.","PeriodicalId":8234,"journal":{"name":"Annual review of chemical and biomolecular engineering","volume":"35 1","pages":""},"PeriodicalIF":7.6000,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deductive Machine Learning Challenges and Opportunities in Chemical Applications\",\"authors\":\"Tianfan Jin, Brett M. Savoie\",\"doi\":\"10.1146/annurev-chembioeng-100722-111917\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive, reasoning is required and still depends on manual intervention by an expert. This review describes the characteristics of deductive reasoning that are helpful for understanding the role played by expert intervention in problem-solving and explains why such interventions are often relatively resistant to disruption by typical machine learning strategies. The article then discusses the factors that contribute to creating a deductive bottleneck, how deductive bottlenecks are currently addressed in several application areas, and how machine learning models capable of deduction can be designed. The review concludes with a tutorial case study that illustrates the challenges of deduction problems and a notebook for readers to experiment with on their own.\",\"PeriodicalId\":8234,\"journal\":{\"name\":\"Annual review of chemical and biomolecular engineering\",\"volume\":\"35 1\",\"pages\":\"\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2024-04-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual review of chemical and biomolecular engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1146/annurev-chembioeng-100722-111917\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual review of chemical and biomolecular engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1146/annurev-chembioeng-100722-111917","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
Deductive Machine Learning Challenges and Opportunities in Chemical Applications
Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive, reasoning is required and still depends on manual intervention by an expert. This review describes the characteristics of deductive reasoning that are helpful for understanding the role played by expert intervention in problem-solving and explains why such interventions are often relatively resistant to disruption by typical machine learning strategies. The article then discusses the factors that contribute to creating a deductive bottleneck, how deductive bottlenecks are currently addressed in several application areas, and how machine learning models capable of deduction can be designed. The review concludes with a tutorial case study that illustrates the challenges of deduction problems and a notebook for readers to experiment with on their own.
期刊介绍:
The Annual Review of Chemical and Biomolecular Engineering aims to provide a perspective on the broad field of chemical (and related) engineering. The journal draws from disciplines as diverse as biology, physics, and engineering, with development of chemical products and processes as the unifying theme.