PN 6A-3

Advanced learning strategies for potential energy surfaces applied to organic electrolytes

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1 to 3 of 3 Results

Replication Data for: How to Train a Shallow Ensemble Aug 12, 2026 Schäfer, Moritz; Kellner, Matthias; Kästner, Johannes; Ceriotti, Michele, 2026, "Replication Data for: How to Train a Shallow Ensemble", https://doi.org/10.18419/DARUS-6366, DaRUS, V1 This repository accompanies our paper on "How to Train a Shallow Ensemble". It includes workflows and input files needed to reproduce the results. Most of the experiments are contained in the directory "0_convergence_nll_training". There is a separate directory for the experiments performed for the first revision of the publication and for the repe...
Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials Aug 11, 2026 Schäfer, Moritz; Kästner, Johannes, 2026, "Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials", https://doi.org/10.18419/DARUS-6365, DaRUS, V1 This repository contains the data for our paper on "Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials". This includes both the simulation results as well as workflows and input files needed to recreate the results. Different experiments are contained on different branches, an...
Replication Data for: Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials Mar 10, 2026 Schäfer, Moritz; Segreto, Nico; Zills, Fabian; Holm, Christian; Kästner, Johannes, 2026, "Replication Data for: Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials", https://doi.org/10.18419/DARUS-5007, DaRUS, V1 This repository contains the data for our paper on "Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials". This includes both the simulation results as well as workflows and input files needed to recreate the results. Different experiments are contained on different branches and a more detailed REA...

Replication Data for: How to Train a Shallow Ensemble

Aug 12, 2026

Schäfer, Moritz; Kellner, Matthias; Kästner, Johannes; Ceriotti, Michele, 2026, "Replication Data for: How to Train a Shallow Ensemble", https://doi.org/10.18419/DARUS-6366, DaRUS, V1

This repository accompanies our paper on "How to Train a Shallow Ensemble". It includes workflows and input files needed to reproduce the results. Most of the experiments are contained in the directory "0_convergence_nll_training". There is a separate directory for the experiments performed for the first revision of the publication and for the repe...

Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials

Aug 11, 2026

Schäfer, Moritz; Kästner, Johannes, 2026, "Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials", https://doi.org/10.18419/DARUS-6365, DaRUS, V1

This repository contains the data for our paper on "Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials". This includes both the simulation results as well as workflows and input files needed to recreate the results. Different experiments are contained on different branches, an...

Replication Data for: Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials

Mar 10, 2026

Schäfer, Moritz; Segreto, Nico; Zills, Fabian; Holm, Christian; Kästner, Johannes, 2026, "Replication Data for: Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials", https://doi.org/10.18419/DARUS-5007, DaRUS, V1

This repository contains the data for our paper on "Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials". This includes both the simulation results as well as workflows and input files needed to recreate the results. Different experiments are contained on different branches and a more detailed REA...

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