Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems

• Published in: Journal of chemical theory and computation 2021-05, Vol.17 (5), p.2641-2658
• Main Authors: Böselt, Lennard, Thürlemann, Moritz, Riniker, Sereina
• Format: Article
• Language: English
• Subjects: Accuracy; Data points; Density functional theory; Dynamics; Electronic structure; Machine learning; Mathematical models; Molecular dynamics; Neural networks; Parameters; Quantum mechanics; Regularization; Retinoic acid; Self consistent fields; Simulation; Workflow
• ISSN: 1549-9618; 1549-9626
• DOI: 10.1021/acs.jctc.0c01112

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.