用于催化剂的有机联体的综合发现平台
Tobias Gensch1,2, Gabriel Dos Passos Gomes3,4,5, Pascal Friederich3,4,6
1Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112, United States.
Journal of the American Chemical Society
|January 12, 2022
概括
这项研究介绍了Kraken,一个设计分子催化剂的平台. 它使用机器学习预测超过30万个有机联体的特性, 加快催化剂的发现.
科学领域:
- 计算化学
- 材料科学
- 催化剂
背景情况:
- 设计分子催化剂是复杂的,通常依赖于直觉和有限的搜索.
- 预测催化剂的性能对于导航广的化学空间至关重要.
研究的目的:
- 推出Kraken,一个数据驱动的平台,用于发现有机...
- 使用机器学习开发催化剂性能预测模型.
主要方法:
- 使用量子力学方法计算了1558种有机 (III) 配体的物理化学描述值.
- 训练机器学习模型预测超过30万个新连接体的属性.
- 开发了一个平台 (Kraken) 来探索联体属性空间.
主要成果:
- 为大量的配体生成了全面的描述符.
- 构建精确的机器学习模型来预测配体特性.
- 证明了有机联体属性空间的系统探索.
结论:
- 克拉肯加速了新型分子催化剂的发现和选择.
- 数据驱动的方法和预测模型是有效的催化剂设计的关键.
- 与现有的催化数据相结合,可增强对反应优化的配体选择.
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