QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules
Abstract Progress in both Machine Learning (ML) and Quantum Chemistry (QC) methods
have resulted in high accuracy ML models for QC properties. Datasets such as MD17 and …
have resulted in high accuracy ML models for QC properties. Datasets such as MD17 and …
Assessing non-nested configurations of multifidelity machine learning for quantum-chemical properties
Multifidelity machine learning (MFML) for quantum chemical properties has seen strong
development in the recent years. The method has been shown to reduce the cost of …
development in the recent years. The method has been shown to reduce the cost of …
A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements
We present a machine-learned (ML) model of kinetic energy for orbital-free density
functional theory (OF-DFT) suitable for bulk light weight metals and compounds made of …
functional theory (OF-DFT) suitable for bulk light weight metals and compounds made of …
Multi-fidelity information fusion for turbulent transport modeling in magnetic fusion plasma
Maintaining the high-temperature and pressure conditions required for sustained nuclear
fusion is challenging due to the turbulent transport that naturally occurs in the plasma …
fusion is challenging due to the turbulent transport that naturally occurs in the plasma …
[PDF][PDF] Single-and Multi-Fidelity Gaussian Process Regression Models with Uncertain Inputs
J Walker - mediatum.ub.tum.de
Gaussian process (GP) models are widely used for regression tasks. Recently, several
authors have developed GP models that fuse information from sources of different fidelity …
authors have developed GP models that fuse information from sources of different fidelity …