Abstract
Emerging machine learning (ML)-based approaches provide powerful and novel tools to study a variety of physical and chemical problems. In this contribution, we outline a universal strategy to create ML-based atomistic force fields, which can be used to perform high-fidelity molecular dynamics simulations. This scheme involves (1) preparing a big reference dataset of atomic environments and forces with sufficiently low noise, e.g., using density functional theory or higher-level methods, (2) utilizing a generalizable class of structural fingerprints for representing atomic environments, (3) optimally selecting diverse and non-redundant training datasets from the reference data, and (4) proposing various learning approaches to predict atomic forces directly (and rapidly) from atomic configurations. From the atomistic forces, accurate potential energies can then be obtained by appropriate integration along a reaction coordinate or along a molecular dynamics trajectory. Based on this strategy, we have created model ML force fields for six elemental bulk solids, including Al, Cu, Ti, W, Si, and C, and show that all of them can reach chemical accuracy. The proposed procedure is general and universal, in that it can potentially be used to generate ML force fields for any material using the same unified workflow with little human intervention. Moreover, the force fields can be systematically improved by adding new training data progressively to represent atomic environments not encountered previously.
Highlights
The most direct computational strategy to monitor the atomiclevel time-evolution of chemical and physical processes is by the molecular dynamics (MD) method
Atomic configurations and forces were obtained from several density functional theory (DFT)-based MD simulations performed for bulk Al, Cu, Ti, W, Si, and C at a sufficient level of accuracy
We summarize our findings for these baseline machine learning (ML) force fields
Summary
The most direct computational strategy to monitor the atomiclevel time-evolution of chemical and physical processes is by the molecular dynamics (MD) method. The atomic forces at each time-step may be obtained either using quantum mechanics-based methods, such as density functional theory (DFT), or parameterized classical force fields. The repetitive and expensive DFT force computations during MD and the necessary small MD time steps (of the order of femtoseconds), lead to the primary bottlenecks of DFTbased MD simulations. Parameterized classical force fields (which are 6–10 orders of magnitude faster than DFT) may be used to access truly long time scales and large length scales. These approaches are not satisfactory either, as such force fields lack accuracy and versatility, i.e., they are not transferable to situations that were not originally used in the parameterization
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