International audienceWe investigate the use of invariant polynomials in the construction of data-driven interatomic potentials for material systems. The "atomic body-ordered permutation-invariant polynomials" (aPIPs) comprise a systematic basis and are constructed to preserve the symmetry of the potential energy function with respect to rotations and permutations. In contrast to kernel based and artificial neural network models, the explicit decomposition of the total energy as a sum of atomic body-ordered terms allows to keep the dimensionality of the fit reasonably low, up to just 10 for the 5-body terms. The explainability of the potential is aided by this decomposition, as the low body-order components can be studied and interpreted in...
The prediction of chemical properties using Machine Learning (ML) techniques calls for a set of appr...
Artificial neural networks are fitted to molecular dynamics trajectories using the Behler-Parrinello...
Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generatin...
International audienceWe investigate the use of invariant polynomials in the construction of data-dr...
We investigate the use of invariant polynomials in the construction of data-driven interatomic poten...
We introduce and explore an approach for constructing force fields for small molecules, which combin...
International audienceWe introduce and explore an approach for constructing force fields for small m...
The accurate representation of multidimensional potential energy surfaces is a necessary requirement...
We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations ove...
The accurate representation of multidimensional potential energy surfaces is a necessary requirement...
We describe a procedure to develop a fitting basis for molecular potential energy surfaces (PES) tha...
A more flexible neural network (NN) method using the fundamental invariants (FIs) as the input vecto...
International audienceThe Atomic Cluster Expansion (Drautz, Phys. Rev. B 99, 2019) provides a framew...
We explore different ways to simplify the evaluation of the smooth overlap of atomic positions (SOAP...
Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundament...
The prediction of chemical properties using Machine Learning (ML) techniques calls for a set of appr...
Artificial neural networks are fitted to molecular dynamics trajectories using the Behler-Parrinello...
Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generatin...
International audienceWe investigate the use of invariant polynomials in the construction of data-dr...
We investigate the use of invariant polynomials in the construction of data-driven interatomic poten...
We introduce and explore an approach for constructing force fields for small molecules, which combin...
International audienceWe introduce and explore an approach for constructing force fields for small m...
The accurate representation of multidimensional potential energy surfaces is a necessary requirement...
We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations ove...
The accurate representation of multidimensional potential energy surfaces is a necessary requirement...
We describe a procedure to develop a fitting basis for molecular potential energy surfaces (PES) tha...
A more flexible neural network (NN) method using the fundamental invariants (FIs) as the input vecto...
International audienceThe Atomic Cluster Expansion (Drautz, Phys. Rev. B 99, 2019) provides a framew...
We explore different ways to simplify the evaluation of the smooth overlap of atomic positions (SOAP...
Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundament...
The prediction of chemical properties using Machine Learning (ML) techniques calls for a set of appr...
Artificial neural networks are fitted to molecular dynamics trajectories using the Behler-Parrinello...
Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generatin...