Supplemental Material: Hierarchical quantum embedding by machine learning for large molecular assemblies

Supplemental Material: Hierarchical quantum embedding by machine learning for large molecular assemblies This archive contains all software and data to reproduce the results of Hierarchical quantum embedding by machine learning for large molecular assemblies, Moritz Bensberg, Marco Eckhoff, Raphael T. Husistein, Matthew S. Teynor, Valentina Sora, William Bro-Jørgensen, F. Emil Thomasen, Anders Krogh, Kresten Lindorff-Larsen, Gemma C. Solomon, Thomas Weymuth, and Markus Reiher, **to be submitted**, 2025. Note that for reproducing the transfer learning, the base machine learning potential provided in Ref. [1] is required. Citation-------------Please cite this repository and Ref. [2] when reusing the data. Databases-----------------├──model-complex-shrunk-QMQM.tar.xz : The database containing all QM/MM and QM/QM/MM results for the MCL1-19G protein-ligand complex. | ├──calculations.bson : The calculations collection exported as a bson file. To reimport it into a running mongo database use `mongoimport`. The calculation objects correspond to input and output of individual single point calculations. | ├──calculations.metadata.json : The metadata of the calculations collection. | ├──properties.bson : The property collection (i.e., collection of properties such as energies, charges etc.) | ├──properties.metadata.json | ├──structures.bson : The structure collection (i.e., collection of all cartesian coordinates and element symbols). | └──structures.metadata.json├──model-ligand-solvent-QMQM.tar.xz : The database containing all QM/MM and QM/QM/MM results for the solvated 19G ligand. | ├──calculations.bson : The calculations collection exported as a bson file. To reimport it into a running mongo database use `mongoimport`. The calculation objects correspond to input and output of individual single point calculations. | ├──calculations.metadata.json : The metadata of the calculations collection. | ├──properties.bson : The property collection (i.e., collection of properties such as energies, charges etc.) | ├──properties.metadata.json | ├──structures.bson : The structure collection (i.e., collection of all cartesian coordinates and element symbols). | └──structures.metadata.json Machine Learning Potentials (MLPs)-----------------------------------------------------------├──machine_learning_potentials.tar.gz | ├──model-complex-shrunk-QMQM.tar.gz : MLP for the solvated MCL1-19G protein-ligand complex. | └──model-ligand-solvent-QMQM.tar.gz : MLP for the solvated 19G ligand. Work Distributions------------------------------├──work_distributions.tar.gz | ├──model-complex-shrunk-QMQM.tar.gz : Work values for the MCL1-19G protein-ligand complex. | | ├──0-5_output_10ps : Work values for each run with index 0-5. | ├──model-ligand-solvent-QMQM.tar.gz : Work values for the solvated 19G ligand. | | └──0-5_output_10ps : Work values for each run with index 0-5. Software---------------├──software.tar.gz | ├──puffin : Code for the "puffin" clients to be run on a HPC cluster. | ├──swoose : The QM/MM software Swoose. | ├──serenity_wrapper : The SCINE framework wrapper for the quantum chemistry program Serenity. | ├──utils-open-source : The scine_utilities version used in this project. | ├──pipeline : A collection of Python scripts/executables to generate plots, populate the database, and run the active learning. | ├──NEQ_switching_ani2x : Software to run the NEQ switching simulations. | ├──EEForce : OpenMM/MLP interface for element embracing machine learning potentials. | └──SymmetryFunctions : Library to calculate the element embracing symmetry functions for element embracing machine learning potentials. Transfer-Learning Gradient Study------------------------------------------------├──episodic_memory.tar.gz : The data for the study investigating the effect of energy derivatives on the transfer learning. | ├──input.data_q4bio-model-complex-shrunk_MMMM : Contains all of the MCL1-19G protein-ligand complex structures used in the study, including their MM energies and forces. | ├──input.data_q4bio-model-complex-shrunk_MMMM_T : Contains all of the MCL1-19G protein-ligand complex structures used in the study, including their QM energies and forces. | ├──input.data_q4bio-model-ligand_solvent_MMMM : Contains all of the solvated 19G ligand structures used in the study, including their MM energies and forces. | └──input.data_q4bio-model-ligand_solvent_MMMM_T : Contains all of the solvated 19G ligand structures used in the study, including their QM energies and forces.References------------------[1] Supplementary Material: Machine Learning Enhanced Calculation of Quantum-Classical Binding Free Energies, Moritz Bensberg, Marco Eckhoff, F. Emil Thomasen, William Bro-Jørgensen, Matthew S. Teynor, Valentina Sora, Thomas Weymuth, Raphael T. Husistein, Frederik E. Knudsen, Anders Krogh, Kresten Lindorff-Larsen, Markus Reiher, and Gemma C. Solomon, ERDA, 2025, DOI: **archive under construction**, url: https://sid.erda.dk/cgi-sid/ls.py?share_id=L6JhSY0c1P. [2] Hierarchical quantum embedding by machine learning for large molecular assemblies, Moritz Bensberg, Marco Eckhoff, Raphael T. Husistein, Matthew S. Teynor, Valentina Sora, William Bro-Jørgensen, F. Emil Thomasen, Anders Krogh, Kresten Lindorff-Larsen, Gemma C. Solomon, Thomas Weymuth, and Markus Reiher, arXiv 2025, DOI: 10.48550/ARXIV.2503.03928.