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Found 8 papers, 4 papers with code
Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for Electrocatalysis
The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property.
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
Molecular dynamics (MD) employing machine-learned interatomic potentials (MLIPs) serve as an efficient, urgently needed complement to ab initio molecular dynamics (aiMD).
From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery
Materials discovery driven by statistical property models is an iterative decision process, during which an initial data collection is extended with new data proposed by a model-informed acquisition function--with the goal to maximize a certain "reward" over time, such as the maximum property value discovered so far.
Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models
The ultimate precision is reached with an infinitely large basis set, i. e., in the limit of a complete basis set (CBS).
Heat flux for semi-local machine-learning potentials
The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials.
Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence
Heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes, e. g., the different surface chemical reactions, and the dynamic re-structuring of the catalyst material at reaction conditions.
Materials Science
TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions
Mutual information determines the relevance of features in terms of their joint mutual dependence to the property of interest.
New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides
Predicting the stability of the perovskite structure remains a longstanding challenge for the discovery of new functional materials for photovoltaics, fuel cells, and many other applications.
Materials Science
