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 …

Machine-Learning-Driven High-Entropy Alloy Catalyst Discovery to Circumvent the Scaling Relation for CO2 Reduction Reaction

ZW Chen, Z Gariepy, L Chen, X Yao, A Anand… - ACS …, 2022 - ACS Publications
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 …

High-throughput and machine-learning accelerated design of high entropy alloy catalysts

ZW Chen, LX Chen, Z Gariepy, X Yao, CV Singh - Trends in Chemistry, 2022 - cell.com
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 …

[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 …

Recent advances in computational design of structural multi-principal element alloys

A Anand, SJ Liu, CV Singh - Iscience, 2023 - cell.com
Multi-principal element alloys (MPEAs) have gained extensive interest for structural
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 …

[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 …

[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 …

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 …

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 …