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Modelling Chemical Processes in Explicit Solvents with Machine Learning Potentials

Documentation:
Deposited data are essential for interpreting, verifying, and extending the research presented in the manuscript, ensuring transparency for readers. The following datasets are included: 1. Training data labelled with reference energies and forces derived from electronic structure calculations. These files are named in the format *target_system*_*reference_DFT_functional*_*comments*.xyz, where comments refer to selectors or the implicit model used. 2. Partial Nudged Elastic Band (NEB) images utilised to generate the free energy profile, named pneb_images_*target_system*.xyz. 3. Initial and final frames of uphill and downhill trajectories, named init/final_configs_downhill/uphill_traj_*target_system*.xyz. For access to complete trajectory frames, please contact the corresponding author.

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Institution:
University of Oxford
Division:
MPLS
Department:
Chemistry
Sub department:
Physical & Theoretical Chem
Oxford college:
New College
Role:
Creator
More by this author/creator
Institution:
University of Oxford
Division:
MPLS
Department:
Chemistry
Sub department:
Physical & Theoretical Chem
Role:
Creator
More by this author/creator
Institution:
University of Oxford
Division:
MPLS
Department:
Chemistry
Sub department:
Physical & Theoretical Chem
Role:
Creator
ORCID:
0000-0002-6062-8209

Publisher:
University of Oxford
Publication date:
2024
DOI:
EISSN:
2041-1723
ISSN:
2041-1723

Language:
English
Pubs id:
2001170
Local pid:
pubs:2001170
Deposit date:
2024-06-02

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