{"title":"电子停止功率的机器学习预测","authors":"Felipe Bivort","doi":"10.52591/lxai202211281","DOIUrl":null,"url":null,"abstract":"The prediction of Electronic Stopping Power for general ions and targets is a problem that lacks a closed-form solution. While full approximate solutions from first principles exist for certain cases, the most general model in use is a pseudo-empirical model. This paper presents our advances towards creating predictive models that leverage state-of-the-art Machine Learning techniques. A key component of our approach is the training data selection. We show results that outperform or are on par with the current best pseudo-empirical Stopping Power model as quantified by the Mean Absolute Percentage Error metric.","PeriodicalId":266286,"journal":{"name":"LatinX in AI at Neural Information Processing Systems Conference 2022","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Towards a Machine Learning Prediction of Electronic Stopping Power\",\"authors\":\"Felipe Bivort\",\"doi\":\"10.52591/lxai202211281\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The prediction of Electronic Stopping Power for general ions and targets is a problem that lacks a closed-form solution. While full approximate solutions from first principles exist for certain cases, the most general model in use is a pseudo-empirical model. This paper presents our advances towards creating predictive models that leverage state-of-the-art Machine Learning techniques. A key component of our approach is the training data selection. We show results that outperform or are on par with the current best pseudo-empirical Stopping Power model as quantified by the Mean Absolute Percentage Error metric.\",\"PeriodicalId\":266286,\"journal\":{\"name\":\"LatinX in AI at Neural Information Processing Systems Conference 2022\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"LatinX in AI at Neural Information Processing Systems Conference 2022\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.52591/lxai202211281\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"LatinX in AI at Neural Information Processing Systems Conference 2022","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52591/lxai202211281","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards a Machine Learning Prediction of Electronic Stopping Power
The prediction of Electronic Stopping Power for general ions and targets is a problem that lacks a closed-form solution. While full approximate solutions from first principles exist for certain cases, the most general model in use is a pseudo-empirical model. This paper presents our advances towards creating predictive models that leverage state-of-the-art Machine Learning techniques. A key component of our approach is the training data selection. We show results that outperform or are on par with the current best pseudo-empirical Stopping Power model as quantified by the Mean Absolute Percentage Error metric.