6 research outputs found
Hybrid Classical/Machine-Learning Force Fields for the Accurate Description of Molecular Condensed-Phase Systems
Electronic structure methods offer in principle accurate predictions of
molecular properties, however, their applicability is limited by computational
costs. Empirical methods are cheaper, but come with inherent approximations and
are dependent on the quality and quantity of training data. The rise of machine
learning (ML) force fields (FFs) exacerbates limitations related to training
data even further, especially for condensed-phase systems for which the
generation of large and high-quality training datasets is difficult. Here, we
propose a hybrid ML/classical FF model that is parametrized exclusively on
high-quality ab initio data of dimers and monomers in vacuum but is
transferable to condensed-phase systems. The proposed hybrid model combines our
previous ML-parametrized classical model with ML corrections for situations
where classical approximations break down, thus combining the robustness and
efficiency of classical FFs with the flexibility of ML. Extensive validation on
benchmarking datasets and experimental condensed-phase data, including organic
liquids and small-molecule crystal structures, showcases how the proposed
approach may promote FF development and unlock the full potential of classical
FFs
Energy-Based Clustering: Fast and Robust Clustering of Data with Known Likelihood Functions
Clustering has become an indispensable tool in the presence of increasingly
large and complex data sets. Most clustering algorithms depend, either
explicitly or implicitly, on the sampled density. However, estimated densities
are fragile due to the curse of dimensionality and finite sampling effects, for
instance in molecular dynamics simulations. To avoid the dependence on
estimated densities, an energy-based clustering (EBC) algorithm based on the
Metropolis acceptance criterion is developed in this work. In the proposed
formulation, EBC can be considered a generalization of spectral clustering in
the limit of large temperatures. Taking the potential energy of a sample
explicitly into account alleviates requirements regarding the distribution of
the data. In addition, it permits the subsampling of densely sampled regions,
which can result in significant speed-ups and sublinear scaling. The algorithm
is validated on a range of test systems including molecular dynamics
trajectories of alanine dipeptide and the Trp-cage miniprotein. Our results
show that including information about the potential-energy surface can largely
decouple clustering from the sampling density
Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems
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 delta-learning
scheme, where the ML model learns the difference between a reference method
(DFT) and a cheaper semi-empirical method (DFTB). We show that such a scheme
reaches the accuracy of the DFT reference method, while requiring significantly
less parameters. Furthermore, the delta-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 with cytosine in water. The presented
results indicate that delta-learning is a promising approach for (QM)ML/MM MD
simulations of condensed-phase systems
Combining classical force fields and machine learning for the description of condensed phase systems
Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions
Simulations with an explicit description of intermolecular forces using electronic structure methods are still not feasible for many systems of interest. As a result, empirical methods such as force fields (FF) have become an established tool for the simulation of large and complex molecular systems. However, the parametrization of FF is time consuming and has traditionally been based largely on experimental data, which is scarce for many functional groups. Recent years have therefore seen increasing efforts to automatize FF parametrization and a move towards FF fitted against quantum-mechanical reference data. Here, we propose an alternative strategy to parametrize intermolecular interactions, which makes use of machine learning and gradient-descent based optimization while retaining a functional form founded in physics. This strategy can be viewed as generalization of existing FF parametrization methods. In the proposed approach, graph neural networks are used in conjunction with automatic differentiation to parametrize physically motivated models to potential-energy surfaces, enabling full automatization and broad applicability in chemical space. As a result, highly accurate FF models are obtained which retain the computational efficiency, interpretability and robustness of classical FF. To showcase the potential of the proposed method, both a fixed-charge model and a polarizable model are parametrized for intermolecular interactions and applied to a wide range of systems including dimer dissociation curves and condensed-phase systems
Learning Atomic Multipoles: Prediction of the Electrostatic Potential with Equivariant Graph Neural Networks
The accurate description of electrostatic interactions remains a challenging problem for classical potential-energy functions. The commonly used fixed partial-charge approximation fails to reproduce the electrostatic potential at short range due to its insensitivity to conformational changes and anisotropic effects. At the same time, possibly more accurate machine-learned (ML) potentials struggle with the long-range behavior due to their inherent locality ansatz. Employing a multipole expansion offers in principle an exact treatment of the electrostatic potential such that the long-range and short-range electrostatic interactions can be treated simultaneously with high accuracy. However, such an expansion requires the calculation of the electron density using computationally expensive quantum-mechanical (QM) methods. Here, we introduce an equivariant graph neural network (GNN) to address this issue. The proposed model predicts atomic multipoles up to the quadrupole, circumventing the need for expensive QM computations. By using an equivariant architecture, the model enforces the correct symmetry by design without relying on local reference frames. The GNN reproduces the electrostatic potential of various systems with high fidelity. Possible uses for such an approach include the separate treatment of long-range interactions in ML potentials, the analysis of electrostatic potential surfaces, and static multipoles in polarizable force fields.ISSN:1549-9618ISSN:1549-962