2020/10/31 by Lennard Böselt, Moritz Thürlemann, Sereina Riniker · 161 citations
Materials Science · Physics and Astronomy · #Advanced Chemical Physics Studies #Artificial intelligence #Computer science #Electron #Embedding #Force field (fiction) #Machine Learning in Materials Science #Molecular dynamics #Physics #QM/MM #Quantum mechanics #Regularization (linguistics) #Spectroscopy and Quantum Chemical Studies #Statistical physics #Valence electron #physics.bio-ph #physics.chem-ph #physics.comp-ph
paper · pdf · doi:10.1021/acs.jctc.0c01112
published in Journal of Chemical Theory and Computation 17(5), 2641-2658 (American Chemical Society)
arxiv created 2021/02/16 · openalex publication_date 2021/04/05 · arxiv updated 2021/04/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
High Resolution Image Download MS PowerPoint Slide 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.