1 code implementation • 18 Mar 2025 • Arslan Mazitov, Filippo Bigi, Matthias Kellner, Paolo Pegolo, Davide Tisi, Guillaume Fraux, Sergey Pozdnyakov, Philip Loche, Michele Ceriotti
Machine-learning interatomic potentials (MLIPs) have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of the effort.
no code implementations • 3 Feb 2022 • Jigyasa Nigam, Sergey Pozdnyakov, Guillaume Fraux, Michele Ceriotti
Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents.
no code implementations • 18 May 2021 • Alexander Goscinski, Félix Musil, Sergey Pozdnyakov, Michele Ceriotti
For each training dataset and number of basis functions, one can determine a unique basis that is optimal in this sense, and can be computed at no additional cost with respect to the primitive basis by approximating it with splines.
no code implementations • 7 Jul 2020 • Jigyasa Nigam, Sergey Pozdnyakov, Michele Ceriotti
While it has become clear that low-order density correlations do not provide a complete representation of an atomic environment, the exponential increase in the number of possible $N$-body invariants makes it difficult to design a concise and effective representation.
Chemical Physics