Latent Variable Machine Learning Framework for Catalysis: General Models, Transfer Learning, and Interpretability
GO Kayode, MM Montemore - JACS Au, 2023 - ACS Publications
Machine learning has been successfully applied in recent years to screen materials for a
variety of applications. However, despite recent advances, most screening-based machine …
variety of applications. However, despite recent advances, most screening-based machine …
Investigating the Error Imbalance of Large-Scale Machine Learning Potentials in Catalysis
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 …
material dis-covery. The Open Catalyst 2020 (OC20) dataset is one of the largest datasets …
Open challenges in developing generalizable large-scale machine-learning models for catalyst discovery
The development of machine-learned potentials for catalyst discovery has predominantly
been focused on very specific chemistries and material compositions. While they are …
been focused on very specific chemistries and material compositions. While they are …
FINETUNA: fine-tuning accelerated molecular simulations
Progress towards the energy breakthroughs needed to combat climate change can be
significantly accelerated through the efficient simulation of atomistic systems. However …
significantly accelerated through the efficient simulation of atomistic systems. However …
Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation
Computational screening in heterogeneous catalysis relies increasingly on machine
learning models for predicting key input parameters due to the high cost of computing these …
learning models for predicting key input parameters due to the high cost of computing these …
Machine learning for catalysis informatics: recent applications and prospects
T Toyao, Z Maeno, S Takakusagi, T Kamachi… - Acs …, 2019 - ACS Publications
The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …
components to maintaining an ecological balance in the future. Recent revolutions made in …
Methods for comparing uncertainty quantifications for material property predictions
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 …
materials science and catalysis fields. This proliferation has spurned the creation of various …
Machine learning for atomic simulation and activity prediction in heterogeneous catalysis: current status and future
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 …
long been the testing ground of new characterization techniques. Machine learning (ML) as …
Interpretable Machine Learning for Catalytic Materials Design toward Sustainability
Conspectus Finding catalytic materials with optimal properties for sustainable chemical and
energy transformations is one of the pressing challenges facing our society today …
energy transformations is one of the pressing challenges facing our society today …
A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts
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 …
provides an efficient scheme to estimate the chemisorption energies of hydrogenated …