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化学品碳足迹的增强深度学习模型

Dachuan Zhang¹, Zhanyun Wang^1,2, Christopher Oberschelp¹

¹National Centre of Competence in Research (NCCR) Catalysis, Ecological Systems Design, Institute of Environmental Engineering, ETH Zürich, Zürich 8093, Switzerland.

ACS sustainable chemistry & engineering

|February 23, 2024

概括

FineChem 2 准确地预测化学产品的碳足迹 (PCF) 使用一个新的变压器框架. 该工具通过识别PCF密集型组件来增强可持续性评估,有助于设计更绿色的化学品.

科学领域:

环境化学环境化学
计算化学的计算化学
可持续化学可持续化学

背景情况:

关于设计化学品的产品碳足迹 (PCF) 的数据有限,这阻碍了可持续性评估.
现有的PCF预测工具具有狭窄的适用性和有限的准确性,特别是对于新型或多样化的化学结构.

研究的目的:

开发一种新,准确和广泛适用的工具,用于预测化学品的产品碳足迹 (PCF).
通过提高可解释性来改进可持续分子和化学过程的设计.

主要方法:

开发FineChem 2,一个基于新型变压器框架和专有行业数据的预测模型.
在变压器框架内使用注意力机制,以提高PCF预测的可解释性.

主要成果:

与现有方法相比,FineChem 2显示化学PCF的预测能力显著提高.
在FineChem 2的应用领域扩展了约75%跨越多种多样的全球化学品.
该工具成功地识别了PCF密集型子结构和关键原材料,提供了设计见解.

结论:

在精确估计化学PCF方面,FineChem 2提供了显著的进步,解决了关键数据缺口.
FineChem 2 的可解释性为设计更可持续的化学品和优化制造流程提供了宝贵的见解.
该工具支持全球化学工业向更大的可持续性过渡.

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