Ab initio machine learning in chemical compound space
Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …
Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning
The training set of atomic configurations is key to the performance of any Machine Learning
Force Field (MLFF) and, as such, the training set selection determines the applicability of the …
Force Field (MLFF) and, as such, the training set selection determines the applicability of the …
[HTML][HTML] Matrix of orthogonalized atomic orbital coefficients representation for radicals and ions
Chemical (molecular, quantum) machine learning relies on representing molecules in
unique and informative ways. Here, we present the matrix of orthogonalized atomic orbital …
unique and informative ways. Here, we present the matrix of orthogonalized atomic orbital …
[PDF][PDF] Towards self-driving laboratories in chemistry and materials sciences: The central role of DFT in the era of AI
Density functional theory plays a pivotal role for the chemical and materials science due to
its relatively high predictive power, applicability, versatility and low computational cost. We …
its relatively high predictive power, applicability, versatility and low computational cost. We …
Weighted-Average Least Squares for Negative Binomial Regression
K Huynh - arxiv preprint arxiv:2404.11324, 2024 - arxiv.org
Model averaging methods have become an increasingly popular tool for improving
predictions and dealing with model uncertainty, especially in Bayesian settings. Recently …
predictions and dealing with model uncertainty, especially in Bayesian settings. Recently …