Interpretable machine learning for knowledge generation in heterogeneous catalysis
Most applications of machine learning in heterogeneous catalysis thus far have used black-
box models to predict computable physical properties (descriptors), such as adsorption or …
box models to predict computable physical properties (descriptors), such as adsorption or …
Bridging the complexity gap in computational heterogeneous catalysis with machine learning
Heterogeneous catalysis underpins a wide variety of industrial processes including energy
conversion, chemical manufacturing and environmental remediation. Significant advances …
conversion, chemical manufacturing and environmental remediation. Significant advances …
From characterization to discovery: artificial intelligence, machine learning and high-throughput experiments for heterogeneous catalyst design
J Benavides-Hernández, F Dumeignil - ACS Catalysis, 2024 - ACS Publications
This review paper delves into synergistic integration of artificial intelligence (AI) and
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
Machine learning: a new paradigm in computational electrocatalysis
Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms
at an atomic level, and uncovering scientific insights lie at the center of the development of …
at an atomic level, and uncovering scientific insights lie at the center of the development of …
Machine learning for design principles for single atom catalysts towards electrochemical reactions
Machine learning (ML) integrated density functional theory (DFT) calculations have recently
been used to accelerate the design and discovery of heterogeneous catalysts such as single …
been used to accelerate the design and discovery of heterogeneous catalysts such as single …
[HTML][HTML] Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane
Conventional methods for develo** heterogeneous catalysts are inefficient in time and
cost, often relying on trial-and-error. The integration of machine-learning (ML) in catalysis …
cost, often relying on trial-and-error. The integration of machine-learning (ML) in catalysis …
Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence
The design of heterogeneous catalysts is challenged by the complexity of materials and
processes that govern reactivity and by the fact that the number of good catalysts is very …
processes that govern reactivity and by the fact that the number of good catalysts is very …
Achieving digital catalysis: strategies for data acquisition, storage and use
Heterogeneous catalysis is an important area of research that generates data as intricate as
the phenomenon itself. Complexity is inherently coupled to the function of the catalyst and …
the phenomenon itself. Complexity is inherently coupled to the function of the catalyst and …
Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis
T Taniike, A Fujiwara, S Nakanowatari… - Communications …, 2024 - nature.com
The empirical aspect of descriptor design in catalyst informatics, particularly when
confronted with limited data, necessitates adequate prior knowledge for delving into …
confronted with limited data, necessitates adequate prior knowledge for delving into …
Accelerated exploration of heterogeneous CO2 hydrogenation catalysts by Bayesian-optimized high-throughput and automated experimentation
A closed-loop data-driven approach was used to optimize catalyst compositions for the
direct transformation of carbon dioxide (CO 2) into methanol by combining Bayesian …
direct transformation of carbon dioxide (CO 2) into methanol by combining Bayesian …