Towards better efficiency of interatomic linear Machine Learning potentials

International audienceInteratomic machine learning potentials have achieved maturity and became worthwhile alternative to conventional interatomic potentials. In this work we profile some characteristics of linear machine learning methods. Being numerically fast and easy to implement, these methods offer many advantages and appear to be very attractive for large length and time scale calculations. However, we emphasize that in order to be accurate on some target properties these methods eventually yield overfitting. This feature is rather independent of training database and descriptor accuracy. At the same time, the major weakness of these potentials, i.e., lower accuracy with respect to the kernel potentials, proves to be their strength: within the confidence limits of the potential fitting, one can rely on less accurate but faster descriptors in order to boost the numerical efficiency. Here, we propose a hybrid type of atomic descriptor that combines the original forms of radial and spectral descriptors. Flexibility in choice of mixing proportions between the two descriptors ensures a user defined control over accuracy / numerical efficiency of the resulting hybrid descriptor form. The performance and features of the above linear machine learning potentials are investigated for the interatomic interactions in metals of primary importance for fusion and fission applications, Fe and W. The suggested hybrid approach opens many avenues in the field of linear machine learning potentials that up to now are preferentially coupled with more robust and computationally expensive spectral descriptors

On sampling minimum energy path

International audienceSampling the Minimum Energy Path (MEP) between two minima of a system is often hindered by the presence of an energy barrier separating the two metastable states. As a consequence, direct sampling based on Molecular Dynamics or Markov Chain Monte Carlo methods becomes inefficient, the crossing of the energy barrier being associated to a rare event. Augmented sampling methods based on the definition of collective variables or reaction coordinates allow to circumvent this limitation at the price of an arbitrary choice of the dimensionality reduction algorithm.We couple the statistical sampling techniques, namely Metadynamics and Invertible Neural Networks, with autoencoders so as to gradually learn the MEP and the collective variable at the same time. Learning is achieved through a succession of two steps: statistical sampling of the most probable path between the two minima and re-definition of the collective variable from the updated data points. The prototypical Mueller potential with nearly orthogonal minima is employed to demonstrate the ability of such coupling to unravel a complex MEP

Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores

International audienceThis work revises the concept of defects in crystalline solids and proposes a universal strategy for their characterization at the atomic scale using outlier detection based on statistical distances. The proposed strategy provides a generic measure that describes the distortion score of local atomic environments. This score facilitates automatic defect localization and enables a stratified description of defects, which allows to distinguish the zones with different levels of distortion within the structure.This work proposes applications for advanced materials modelling ranging from the surrogate concept for the energy per atom to the relevant information selection for evaluation of energy barriers from the mean force.Moreover, this concept can serve for design of robust interatomic machine learning potentials and high-throughput analysis of their databases. The proposed definition of defects opens up many perspectives for materials design and characterisation, promoting thereby the development of novel techniques in materials science

Quantum-accurate magneto-elastic predictions with classical spin-lattice dynamics

International audienceA data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin model with an ML-IAP. Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed. Both the atomic spin model and the ML-IAP are parametrized on data from first-principles calculations. We demonstrate the efficacy of our data-driven framework across magneto-structural phase transitions by generating a magneto-elastic ML-IAP for α-iron. The combined potential energy surface yields excellent agreement with first-principles magneto-elastic calculations and quantitative predictions of diverse materials properties including bulk modulus, magnetization, and specific heat across the ferromagnetic-paramagnetic phase transition