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首页> 外文期刊>Bulletin of the American Physical Society >APS -Annual Meeting of the APS Four Corners Section- Event - Materials prediction using high-throughput and machine learning techniques
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APS -Annual Meeting of the APS Four Corners Section- Event - Materials prediction using high-throughput and machine learning techniques

机译：APS-APS四角区年会-活动-使用高通量和机器学习技术进行材料预测

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The importance of designing new materials with enhanced properties is vital for mankind to prosper and meet their ever-increasing necessities. The task of searching for new and advanced materials is colossal because of the innumerable combinations of different elements. Material scientists have developed large databases of known materials over the last century. The challenge now is to use data from computer simulations to discover new materials. Here at Brigham Young University (BYU) we have built a large database of alloy simulations. High-throughput and machine learning techniques can be used to leverage the database and discover materials at a faster pace. The high-throughput technique is an intelligent way to interrogate a database for inventing new materials. Machine learning models give a computer the ability to learn about materials without being programmed explicitly. In this talk I’ll give a brief overview of my three year work as a PhD student here at BYU. The talk will focus on two important topics: 1) A high-throughput technique we used to invent new materials called superalloys[1], and 2) a few machine learning techniques we are currently pursing for faster prediction of new materials. Small{[1]Chandramouli Nyshadham, Corey Oses, Jacob E. Hansen, Ichiro Takeuchi,Stefano Curtarolo and Gus L. W. Hart,“A computational high-throughputsearch for new ternary superalloys.” Acta Materialia 122 (2017): 438-447.}
机译：设计具有增强性能的新材料的重要性对于人类繁荣和满足其日益增长的需求至关重要。由于各种元素的无数组合，寻找新的和先进的材料的任务非常艰巨。材料科学家在上个世纪已经开发了大型的已知材料数据库。现在的挑战是使用计算机模拟中的数据来发现新材料。在杨百翰大学（BYU），我们已经建立了一个大型的合金模拟数据库。高通量和机器学习技术可用于利用数据库并以更快的速度发现资料。高通量技术是一种查询数据库以发明新材料的智能方法。机器学习模型使计算机无需明确编程即可学习材料。在本次演讲中，我将简要概述我作为BYU博士生三年的工作。演讲将集中在两个重要主题上：1）我们用来发明新材料的高通量技术称为超级合金[1]，以及2）目前正在寻求的一些机器学习技术，用于更快地预测新材料。 Small {[1] Chandramouli Nyshadham，Corey Oses，Jacob E. Hansen，Ichiro Takeuchi，Stefano Curtarolo和Gus L. W. Hart，“对新的三元高温合金的计算高通量研究。”材料学报122（2017）：438-447。}

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