26 citations · 52 across the 10 of their papers we have counts for
10 papers
Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning
Vivin Vinod, Peter Zaspel
Machine learning has accelerated quantum chemistry but is hindered by the prohibitive cost of generating high fidelity training data. Multifidelity machine learning (MFML) mitigate…
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…
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
Matthias Holzenkamp, Dongyu Lyu, Ulrich Kleinekathöfer +1
Uncertainty estimations for machine learning interatomic potentials (MLIPs) are crucial for quantifying model error and identifying informative training samples in active learning…
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…
Assessing Non-Nested Configurations of Multifidelity Machine Learning for Quantum-Chemical Properties
Vivin Vinod, Peter Zaspel
Multifidelity machine learning (MFML) for quantum chemical (QC) properties has seen strong development in the recent years. The method has been shown to reduce the cost of generati…
QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules
Vivin Vinod, Peter Zaspel
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 WS22 have been used…
Optimized Multifidelity Machine Learning for Quantum Chemistry
Vivin Vinod, Ulrich Kleinekathöfer, Peter Zaspel
Machine learning (ML) provides access to fast and accurate quantum chemistry (QC) calculations for various properties of interest such as excitation energies. It is often the case…
Multi-Fidelity Machine Learning for Excited State Energies of Molecules
Vivin Vinod, Sayan Maity, Peter Zaspel +1
The accurate but fast calculation of molecular excited states is still a very challenging topic. For many applications, detailed knowledge of the energy funnel in larger molecular…