Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems
Open access
Datum
2021-05-11Typ
- Journal Article
Abstract
Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed to simulate molecular systems, where an explicit description of changes in the electronic structure is necessary. However, QM/MM MD simulations are computationally expensive compared to fully classical simulations as all valence electrons are treated explicitly and a self-consistent field (SCF) procedure is required. Recently, approaches have been proposed to replace the QM description with machine-learned (ML) models. However, condensed-phase systems pose a challenge for these approaches due to long-range interactions. Here, we establish a workflow, which incorporates the MM environment as an element type in a high-dimensional neural network potential (HDNNP). The fitted HDNNP describes the potential-energy surface of the QM particles with an electrostatic embedding scheme. Thus, the MM particles feel a force from the polarized QM particles. To achieve chemical accuracy, we find that even simple systems require models with a strong gradient regularization, a large number of data points, and a substantial number of parameters. To address this issue, we extend our approach to a δ-learning scheme, where the ML model learns the difference between a reference method (density functional theory (DFT)) and a cheaper semiempirical method (density functional tight binding (DFTB)). We show that such a scheme reaches the accuracy of the DFT reference method while requiring significantly less parameters. Furthermore, the δ-learning scheme is capable of correctly incorporating long-range interactions within a cutoff of 1.4 nm. It is validated by performing MD simulations of retinoic acid in water and the interaction between S-adenoslymethioniat and cytosine in water. The presented results indicate that δ-learning is a promising approach for (QM)ML/MM MD simulations of condensed-phase systems. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000489162Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Journal of Chemical Theory and ComputationBand
Seiten / Artikelnummer
Verlag
American Chemical SocietyOrganisationseinheit
09458 - Riniker, Sereina Z. / Riniker, Sereina Z.
Förderung
178762 - Passive Membrane-Permeability Prediction for Peptides and Peptidomimetics Using Computational Methods (SNF)
ETH-34 17-2 - Combining Machine Learning and Molecular Dynamics to Predict Physicochemical Quantities of Molecules (ETHZ)
Zugehörige Publikationen und Daten
Is supplemented by: https://doi.org/10.3929/ethz-b-000512374