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Bayesian Linear Regression for Accurate and Efficient Atomistic Machine Learning Models

Engineering and Technology

Bayesian Linear Regression for Accurate and Efficient Atomistic Machine Learning Models

C. V. D. Oord

Discover how C. van der Oord is revolutionizing material property predictions with a groundbreaking Bayesian approach to linear regression that enhances both accuracy and efficiency in atomistic machine learning models, specifically the Accurate and Efficient (ACE) model.

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Playback language: English
Abstract
This paper presents a Bayesian approach to linear regression for fitting parameters in atomistic machine learning models, specifically focusing on the Accurate and Efficient (ACE) model. The method improves the accuracy and efficiency of predicting material properties by incorporating Bayesian inference into the model fitting process.
Publisher
npj Computational Materials
Published On
Jan 31, 2023
Authors
C. van der Oord
Tags
Bayesian inference
linear regression
machine learning
material properties
ACE model
model fitting

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