变压器在化学信息学中的应用
1Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA 93106, United States.
Journal of chemical information and modeling
|May 30, 2024
概括
本综述总结了变压器架构如何适应化学中的机器学习. 它强调了它们在各种化学数据表示中的应用,用于诸如属性预测等任务.
科学领域:
- 计算化学是一种计算化学.
- 机器学习是机器学习.
- 化学领域的人工智能
背景情况:
- 计算加速了化学过程,机器学习 (ML) 识别了对属性预测和物质生成等任务的模式.
- 复杂的化学数据需要先进的ML架构,变压器模型显示出显著的前景.
研究的目的:
- 审查最近应用变压器架构在化学中的机器学习问题的进展.
- 提供现有工程适应变压器用于化学数据分析的综合总结.
主要方法:
- 对化学中变压器架构的出版物的结构化审查.
- 基于各种化学数据表示的分析.
- 突出不同表示和模型的优缺点.
主要成果:
- 变压器在化学中越来越多地被采用,彻底改变了ML应用.
- 变压器的适应性在不同的化学数据表示上有所不同.
- 目前正在进行的研究探讨了各种各样的用例和模型架构.
结论:
- 变压器模型为复杂的化学学习任务提供了强大的解决方案.
- 化学表示的选择对于有效的变压器模型应用至关重要.
- 未来的方向包括进一步探索化学领域的变压器能力.
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