Catalysis in the digital age: Unlocking the power of data with machine learning
BM Abraham, MV Jyothirmai, P Sinha… - Wiley …, 2024 - Wiley Online Library
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 …
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 …
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 …
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 developing accurate and interpretable electronic …
provides a straightforward pathway for developing 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
F Göltl, M Mavrikakis - ChemCatChem, 2022 - Wiley Online Library
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 …