4 papers
LFaB: Low fidelity as Bias for Active Learning in the chemical configuration space
Vivin Vinod, Peter Zaspel
Active learning promises to provide an optimal training sample selection procedure in the construction of machine learning models. It often relies on minimizing the model's varianc…
Excitation Energy Transfer between Porphyrin Dyes on a Clay Surface: A study employing Multifidelity Machine Learning
Dongyu Lyu, Matthias Holzenkamp, Vivin Vinod +5
Natural light-harvesting antenna complexes efficiently capture solar energy using chlorophyll, i.e., magnesium porphyrin pigments, embedded in a protein matrix. Inspired by this na…
Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies
Vivin Vinod, Peter Zaspel
Recent progress in machine learning (ML) has made high-accuracy quantum chemistry (QC) calculations more accessible. Of particular interest are multifidelity machine learning (MFML…
Benchmarking Data Efficiency in -ML and Multifidelity Models for Quantum Chemistry
Vivin Vinod, Peter Zaspel
The development of machine learning (ML) methods has made quantum chemistry (QC) calculations more accessible by reducing the compute cost incurred in conventional QC methods. This…