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可解释的替代学习用于电子材料生成

Zhilong Wang^1,2,3,4, Sixian Liu^1,2, Kehao Tao^1,2

¹National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, China.

|November 1, 2024

概括

我们开发了EMGen,这是一个可解释的AI框架,用于设计具有特定属性的电子材料. EMGen 快速生成具有宽带间隙的氧化等材料,用于先进的光电子和功率电子应用.

科学领域:

材料科学材料科学材料科学
人工智能的人工智能
凝聚物质物理学凝聚物质物理学

背景情况:

开发具有定制性质的新型电子材料仍然是一个重大挑战.
现有的AI模型往往缺乏可解释性,阻碍了有效的材料设计.
需要人工智能工具,可以积极生成满足特定性能要求的材料.

研究的目的:

引入EMGen,这是一个可解释的替代学习框架,用于电子材料的积极设计和生成.
为了证明EMGen在设计具有目标电子性质的材料的能力,特别是带间隙.
展示EMGen在发现和优化光电子和功率电子材料中的应用.

主要方法:

开发一个可解释的替代学习框架,命名为EMGen.
使用EMGen来选元素和分数以获得所需的材料特性.
案例研究侧重于设计具有特定频段间隙的电子材料.
创建一个大型混合功能带隙数据库.

主要成果:

EMGen实现了基准测试的预测准确性,并在仅需1.7分钟的时间内设计了一种具有目标频段差距的材料.
使用EMGen.建立了一个全面的混合功能频段差距数据库.
EMGen成功设计了带宽间隙 (>5.0 eV) 的氧化 (Ga2O3).
设计的Ga2O3证明了对深紫外线 (DUV) 光电子和功率电子的增强性能.

结论:

EMGen是一个有效和可解释的AI工具,用于按需生成电子材料.
该框架允许设计具有改进性能的材料,例如用于DUV应用的宽带间隙.
EMGen促进了光电子和功率电子的材料发现的突破,扩大了Ga2O3.3等材料的适用性.

关键词:

积极学习是积极学习.乐队间隙带间隙乐队间隙电子材料电子材料组合学习组合学习第一个原则是计算计算.机器学习是机器学习.

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