Catalysis in the digital age: Unlocking the power of data with machine learning
The design and discovery of new and improved catalysts are driving forces for accelerating
scientific and technological innovations in the fields of energy conversion, environmental …
scientific and technological innovations in the fields of energy conversion, environmental …
Chemocatalytic production of sorbitol from cellulose via sustainable chemistry–a tutorial review
Y Zhou, RL Smith, X Qi - Green Chemistry, 2024 - pubs.rsc.org
Sorbitol, which is a six carbon polyol typically derived from glucose, is widely used in food,
personal care and pharmaceutical products. Sorbitol production processes that use …
personal care and pharmaceutical products. Sorbitol production processes that use …
Hydrogen spillover‐enhanced heterogeneously catalyzed hydrodeoxygenation for biomass upgrading
Y Geng, H Li - ChemSusChem, 2022 - Wiley Online Library
Hydrodeoxygenation (HDO) is regarded as a promising technology for biomass upgrading
to obtain sustainable and competitive chemicals and fuels. In fact, biomass HDO over …
to obtain sustainable and competitive chemicals and fuels. In fact, biomass HDO over …
Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis
Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards
the rational design of novel catalysts, understanding reactions over surfaces is the most …
the rational design of novel catalysts, understanding reactions over surfaces is the most …
Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization
The use of data science tools to provide the emergence of non-trivial chemical features for
catalyst design is an important goal in catalysis science. Additionally, there is currently no …
catalyst design is an important goal in catalysis science. Additionally, there is currently no …
Discovering catalytic reaction networks using deep reinforcement learning from first-principles
T Lan, Q An - Journal of the American Chemical Society, 2021 - ACS Publications
Determining the reaction pathways, which is central to illustrating the working mechanisms
of a catalyst, is severely hindered by the high complexity of the reaction and the extreme …
of a catalyst, is severely hindered by the high complexity of the reaction and the extreme …
Uncovering electronic and geometric descriptors of chemical activity for metal alloys and oxides using unsupervised machine learning
We show that unsupervised machine learning (ML) using principal-component (PC) analysis
provides a straightforward pathway for develo** accurate and interpretable electronic …
provides a straightforward pathway for develo** accurate and interpretable electronic …
Quantum chemical roots of machine-learning molecular similarity descriptors
In this work, we explore the quantum chemical foundations of descriptors for molecular
similarity. Such descriptors are key for traversing chemical compound space with machine …
similarity. Such descriptors are key for traversing chemical compound space with machine …
Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning
Abstract Brønsted‐Evans‐Polanyi (BEP) relationships, ie, a linear scaling between reaction
and activation energies, lie at the core of computational design of heterogeneous catalysts …
and activation energies, lie at the core of computational design of heterogeneous catalysts …
Autonomous high-throughput computations in catalysis
Autonomous atomistic computations are excellent tools to accelerate the development of
heterogeneous (electro-) catalysts. In this perspective, we critically review the achieved …
heterogeneous (electro-) catalysts. In this perspective, we critically review the achieved …