.png){kind=icon}
返回
Thermodynamics-Guided Machine Learning Framework with a Multiobjective Optimizer for Catalyst Discovery
.png){kind=icon}
PRE
AI
DOI：10.1021/acscatal.5c07191.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine learning (ML) holds great promise for discovering catalysts; however, simultaneously possessing high interpretability, prediction accuracy, and catalyst discovery efficiency remains a substantial challenge. Here, an ML framework with thermodynamic guidelines from scratch is constructed to explore superior multimetallic catalysts for the representative reverse water-gas shift (RWGS) reaction. A normalization approach is employed to redefine the elemental physicochemical properties, leveraging the advantages of the elemental and property features of active metals. This not only enables accurate predictions but also derives the value range of each feature through deep insights into the catalytic mechanisms. More importantly, a genetic algorithm (GA)-based multiobjective optimizer is developed for reverse engineering the compositions of multimetallic catalysts. Specifically, by enforcing explicit thermodynamic constraints and self-correcting by experimentally validated results into the ML models, both rigorous discovery of catalysts within the training data set and extrapolation are successfully achieved. After two rounds of self-corrections, optimal ternary-metallic catalysts are experimentally validated with a superior CO yield close to the equilibrium limitation. Therefore, this ML framework is a promising paradigm for catalyst research, as highlighted by intrinsic theoretical guidance and experimental validation.
Keyword:
machine learning
reverse water-gas shift
thermodynamics-guided
multiobjective optimizer
normalization approach
期刊
IF:
13.1
论文数:
1.6W
被引数:
15.0W
机构
暂无机构信息
引用论文
Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane用于甲烷干重整催化剂性能预测和验证的可解释机器学习框架
OAAI
OAAI分享收藏Low-Electronegativity Mn-Contraction of PtMn Nanodendrites Boosts Oxygen Reduction DurabilityPtMn纳米枝晶的低电负性Mn收缩提高了氧还原耐久性
PREAI
分享收藏Developing machine learning for heterogeneous catalysis with experimental and computational data使用实验和计算数据开发用于多相催化的机器学习.png)
Nature Reviews Chemistry
Nature Reviews ChemistryIF51.7
PREAI
分享收藏From CO₂ to DME: Catalytic advances; challenges; and alternatives to conventional gas-phase routes从CO₂到DME：催化进展、挑战及对传统气相路线的替代方案Catal. Sci. Technol.
OAAI
Catal. Sci. Technol.IF4.2
OAAI分享收藏Machine learning for CO2 conversion driven by dielectric barrier discharge plasma and Cs2TeCl6 photocatalystsGREEN CHEMISTRY
GREEN CHEMISTRYIF9.2
PREAI
分享收藏A roadmap from the bond strength to the grain-boundary energies and macro strength of metals从金属键强度到晶界能和宏观强度的路线图NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏
查看更多内容