Applications of machine learning in alloy catalysts: rational selection and future development of descriptors
Z Yang, W Gao - Advanced Science, 2022 - Wiley Online Library
At present, alloys have broad application prospects in heterogeneous catalysis, due to their
various catalytic active sites produced by their vast element combinations and complex …
various catalytic active sites produced by their vast element combinations and complex …
Machine-Learning-Driven High-Entropy Alloy Catalyst Discovery to Circumvent the Scaling Relation for CO2 Reduction Reaction
To achieve an equitable energy transition toward net-zero 2050 goals, the electrochemical
reduction of CO2 (CO2RR) to chemical feedstocks through utilizing both CO2 and …
reduction of CO2 (CO2RR) to chemical feedstocks through utilizing both CO2 and …
High-throughput and machine-learning accelerated design of high entropy alloy catalysts
High entropy alloy (HEA) catalysts have attracted widespread attention due to their high
catalytic performance. Herein, we briefly describe the advantages and challenges of HEA …
catalytic performance. Herein, we briefly describe the advantages and challenges of HEA …
[HTML][HTML] Complex amorphous oxides: property prediction from high throughput DFT and AI for new material search
MJ van Setten, HFW Dekkers, C Pashartis… - Materials …, 2022 - pubs.rsc.org
With decreasing dimensions and increasing complexity, semiconductor devices are getting
more difficult to fabricate. In particular the allowed deposition temperature becomes lower …
more difficult to fabricate. In particular the allowed deposition temperature becomes lower …
Recent advances in computational design of structural multi-principal element alloys
Multi-principal element alloys (MPEAs) have gained extensive interest for structural
applications owing to their excellent strength, fracture toughness, wear resistance, creep …
applications owing to their excellent strength, fracture toughness, wear resistance, creep …
GAASP: Genetic Algorithm-Based Atomistic Sampling Protocol for High-Entropy Materials
G Anand - Materials and Manufacturing Processes, 2023 - Taylor & Francis
High-entropy materials are composed of multiple elements on comparatively simpler lattices.
Due to the multi-component nature of such materials, atomic-scale sampling is …
Due to the multi-component nature of such materials, atomic-scale sampling is …
[HTML][HTML] Computational materials discovery
J Roberts, E Zurek - The Journal of Chemical Physics, 2022 - pubs.aip.org
Tremendous advances in first-principles program packages, spectacular speed-ups in
computer hardware coupled with significant algorithmic developments in crystal structure …
computer hardware coupled with significant algorithmic developments in crystal structure …
[PDF][PDF] Complex amorphous oxides: property prediction from high throughput DFT and AI for new material search
MJ van Setten, HFW Dekkers, C Pashartis… - Materials …, 2022 - lirias.kuleuven.be
With decreasing dimensions and increasing complexity, semiconductor devices are getting
more difficult to fabricate. In particular the allowed deposition temperature becomes lower …
more difficult to fabricate. In particular the allowed deposition temperature becomes lower …
Design of Multi-component Alloy Catalysts Aided by Density Functional Theory and Machine Learning
Z Gariepy - 2023 - search.proquest.com
Catalysts are a key pillar in countless industrial processes and represent key components to
some of the worlds most important solutions regarding climate change. As a result …
some of the worlds most important solutions regarding climate change. As a result …
Machine Learning-Assisted Computational Exploration of High Entropy Materials for Hydrogen Energy
EA Halpren - 2023 - search.proquest.com
Hydrogen fuel can be generated cleanly and used to store and transport energy. Challenges
remain regarding the generation and storage of hydrogen, wherein low-cost materials with …
remain regarding the generation and storage of hydrogen, wherein low-cost materials with …