Frédéric Célerse¹, Veronika Juraskova¹, Shubhajit Das¹, Matthew D. Wodrich^1,2*, Clémence Corminboeuf^1,2,3*

1 Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland

2 National Center for Competence in Research-Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

3 National Centre for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

* Corresponding authors emails: matthew.wodrich@epfl.ch, clemence.corminboeuf@epfl.ch

DOI10.24435/materialscloud:fq-k5 [version v1]

Publication date: Sep 09, 2024

How to cite this record

Frédéric Célerse, Veronika Juraskova, Shubhajit Das, Matthew D. Wodrich, Clémence Corminboeuf, Capturing dichotomic solvent behavior in solute–solvent reactions with neural network potentials, Materials Cloud Archive 2024.135 (2024), https://doi.org/10.24435/materialscloud:fq-k5

Description

Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time-step integration. These NNPs serve to explore a puzzling solute--solvent reactivity route involving the ring opening of N-enoxyphthalimide experimentally observed in methanol but not in 2,2,2-trifluoroethanol (TFE). This reaction represents a challenging example characterized by intricate hydrogen bonding networks and structurally ambiguous solvent-sensitive transition states. The methodology successfully delivers detailed free energy surfaces and relative energy barriers in quantitative agreement with experiment. These barriers are associated with an ensemble of transition states involving direct participation of up to five solvent molecules. While this picture contrasts with the single transition state structure assumed by current static models, no drastic qualitative difference is observed between the formed hydrogen bonding networks and the number of participating solvent molecules in methanol or TFE. The dichotomy between the two solvents thus essentially arises from an electronic effect (i.e., distinct nucleophilicity) and from the larger conformational entropy contributions in methanol. This example underscores the critical role dynamic simulations at the ab initio levels play in capturing the full complexity of solute-solvent interactions. The files used in our studies are listed below, ensuring reproducibility and providing resources for future studies related to this work.

Materials Cloud sections using this data

Files

File name	Size	Description
CP2K.zip MD5md5:b918f5c9a569d2c5a9defbeaafbaef1d	2.6 KiB	CP2K input files for reproducing PBE-D3BJ and PBE0-D3BJ computations
DeepMD_training_example.json MD5md5:97c884a79c000eb89dafff0c8cd0ff49	1.4 KiB	json file for doing NN trainings with the DeepMD software
MD_with_lammps.zip MD5md5:9de6fbca938ac06c650f048a9ed1abbd	19.5 KiB	Files for running MD simulations with the Lammps software
MTS-MD.zip MD5md5:96c22e421d08a9e40a601792cc91dead	159.4 KiB	Files for running MTS simulations
NNPs.zip MD5md5:e58cad381f46c46676ce060224d47153	1.2 GiB	NNPs files for PBE-D3BJ and PBE0-D3BJ (in .pb format) used in our NN-MD simulations
PBE_databases.zip MD5md5:e447b0f236110edc79bd612ee32f5ffb	1.3 GiB	PBE-D3BJ databases used for MeOH and TFE, in raw format suitable for DeepMD
PBE0_databases.zip MD5md5:7ea1477054487befe46beaaf14f797bb	921.4 MiB	PBE0-D3BJ databases used for MeOH and TFE, in raw format suitable for DeepMD
PLUMED.zip MD5md5:e747f83e10e05f55f7afb8e36e57e98f	32.9 KiB	Plumed files with examples used in this study
MeOH-chemiscope.json.gz MD5md5:10af919a44aacad9c4357e9281440f13 Visualize on Chemiscope	133.3 MiB	Chemiscope file containing the structures represented in Figure 5 of the related article
TFE-chemiscope.json.gz MD5md5:afab6c9f8750a35113c9f95a94f29de7 Visualize on Chemiscope	104.5 MiB	Chemiscope file containing the structures represented in Figure 6 of the related article
create_chemiscope.py MD5md5:921b814009cf4765b8e5e95858a21e03	1001 Bytes	Example file used to create the chemiscope files

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

machine learning molecular dynamics simulation free energy ERC