Advanced learning strategies for potential energy surfaces applied to organic electrolytes
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erbs_data.tar.part.005
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: 8f63333d87cfe65c2c05af0e2e6c12bf
Data
split archive part erbs_data.tar.part.005
erbs_data.tar.part.006
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: 76cf2a7dfcf7d5166ab58287aeda1d3e
Data
split archive part erbs_data.tar.part.006
erbs_data.tar.part.007
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: daf658ba253ce2d48e160d29b8d9ddc6
Data
split archive part erbs_data.tar.part.007
erbs_data.tar.part.008
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: ce1cb19b747590eb69704e513b8a4e5c
Data
split archive part erbs_data.tar.part.008
erbs_data.tar.part.009
Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials
Unknown - 5.3 GB - MD5: 25d778b9b7fca5294e4024de8126cc8e
Data
split archive part erbs_data.tar.part.009
erbs_inputs.tar.zst
Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials
Unknown - 95.7 MB - MD5: cae2422ed3ec42ab536af5a45d8fa788
Data
complete repository accompanying the manuscript
README.md
Aug 11, 2026 - Replication Data for: Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials
Markdown Text - 894 B - MD5: c80d70e8158e13586e05f61b06b2551a
Data
complete repository accompanying the manuscript
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...
apax_paper.tar.gz
Mar 10, 2026 - Replication Data for: Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials
Gzip Archive - 55.2 GB - MD5: 3ec04c90fedc713737e901dcafdc0742
Data
complete repository accompaniying the manuscript
README.md
Mar 10, 2026 - Replication Data for: Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials
Markdown Text - 1.7 KB - MD5: 6c629d0ba2c2458d0715e224f841059c
Explanations for the content and structure of the repository.
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