Advanced learning strategies for potential energy surfaces applied to organic electrolytes
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1 to 10 of 20 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...
train_atoms_bmim_nod3.extxyz
Aug 12, 2026 - Replication Data for: How to Train a Shallow Ensemble
Unknown - 51.1 MB - MD5: a82b6a68bedf20e5c03657fb0e99ad8f
Data
complete repository accompanying the manuscript
uncertainty_project.tar.zst
Aug 12, 2026 - Replication Data for: How to Train a Shallow Ensemble
Unknown - 1.2 GB - MD5: e6adb5cfd217cf5536bf95552c111bec
Data
complete repository accompanying the manuscript
val_atoms_bmim_nod3.extxyz
Aug 12, 2026 - Replication Data for: How to Train a Shallow Ensemble
Unknown - 3.0 MB - MD5: 9e0f05173fe02f700cafc1e2a8ebd988
Data
complete repository accompanying the manuscript
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...
erbs_data.tar.part.000
Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials
Unknown - 10.0 GB - MD5: 3a0d9c7019716c0c3ce1399a9fed75f0
Data
split archive part erbs_data.tar.part.000
erbs_data.tar.part.001
Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials
Unknown - 10.0 GB - MD5: 9602b1a600a440bfaf330f2fcfffdcdb
Data
split archive part erbs_data.tar.part.001
erbs_data.tar.part.002
Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials
Unknown - 10.0 GB - MD5: 56367883ed86eaf9d62f347a61472e5d
Data
split archive part erbs_data.tar.part.002
erbs_data.tar.part.003
Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials
Unknown - 10.0 GB - MD5: 5a804bc357e0dfb84128bd25793d16f1
Data
split archive part erbs_data.tar.part.003
erbs_data.tar.part.004
Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials
Unknown - 10.0 GB - MD5: c0f404f040c0e2718ba703ca20732942
Data
split archive part erbs_data.tar.part.004
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