Machine learning potentials (MLPs) have greatly accelerated atomistic simulations for material dis-covery. The Open Catalyst 2020 (OC20) dataset is one of the largest datasets …
The development of machine-learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While they are …
Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomistic systems. However …
Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these …
The discovery and development of catalysts and catalytic processes are essential components to maintaining an ecological balance in the future. Recent revolutions made in …
Data science and informatics tools have been proliferating recently within the computational materials science and catalysis fields. This proliferation has spurned the creation of various …
S Ma, ZP Liu - ACS Catalysis, 2020 - ACS Publications
Heterogeneous catalysis, for its industrial importance and great complexity in structure, has long been the testing ground of new characterization techniques. Machine learning (ML) as …
H Xin, T Mou, HS Pillai, SH Wang… - Accounts of Materials …, 2023 - ACS Publications
Conspectus Finding catalytic materials with optimal properties for sustainable chemical and energy transformations is one of the pressing challenges facing our society today …
O Mamun, KT Winther, JR Boes… - npj Computational …, 2020 - nature.com
For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated …