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Historically, these forces are obtained from empirical potentials (“force fields”) On the atoms in the system, i.e., the negative gradient of the potential energy surface (PES). To sample the phase space of the model system, requires access to the forces acting Numerical integration of Newton’s equations of motion, one time step at a time, Such simulations to understand complex systems has increased steadily, driven byĪdvances in computer architectures and the availability of more accurate mathematicalįunctions describing interatomic interactions. 1 In the last few decades our reliance on Studying the dynamic motion of atoms in molecular and material systems withįemtosecond resolution is made possible via molecular dynamics (MD) Of descriptor and ML algorithm and brings us another step closer to fullyĪutomated ML-PES generation. This approach can be trivially extended to other combinations Significantly reducing the number of ML models needed to be trained to obtain More efficient than optimizing all HPs at the same time by means of Optimization of the HPs in the training stage. To the feature extraction stage are optimized first, followed by the We propose a two-step optimization strategy in which the HPs related Toy C dimer, amorphous carbon, α-Fe, and small organic molecules (QM9ĭataset). Of atomic positions (SOAP) descriptor in combination with GPR-based GaussianĪpproximation potentials (GAP) and optimize HPs for four distinct systems: a Generation using a custom-coded parallel particle swarm optimizer (availableįreely at ). In this paper, we explore HP optimization strategies tailored for ML-PES To ensure the high quality of the resulting ML-PES model. Choosing optimal values for the two sets of HPs is critical Regression (GPR) is used to model the structure-PES relationship based onĪnother set of HPs. Subsequently, an ML algorithm such as neural networks or Gaussian process This representation can beįine-tuned by adjusting on a set of so-called "hyper-parameters" (HPs). Symmetry-invariant mathematical representation. The feature extraction stage transforms atomic positions into a Two stages: feature extraction and structure-property relationship modeling. Training an ML-PES is typically performed in Surfaces (PES) using machine learning (ML) approaches is becoming popular in Modeling non-empirical and highly flexible interatomic potential energy