4 papers
Polarisation, Born Effective Charges, and Topological Invariants via a Berry-Phase Approach
Christian Carbogno, Nikita Rybin, Sara Panahian Jand +4
This paper represents one contribution to a larger Roadmap article reviewing the current status of the FHI-aims code. In this contribution, the implementation of polarization, Born…
Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning
Olga Klimanova, Nikita Rybin, Alexander Shapeev
We present an algorithm for accelerating the search of molecule's adsorption site based on global optimization of surface adsorbate geometries. Our approach uses a machine-learning…
Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential
Nikita Rybin, Ivan S. Novikov, Alexander Shapeev
Inspired by the recent success of machine-learned interatomic potentials for crystal structure prediction of the inorganic crystals, we present a methodology that exploits Moment T…
Towards high-throughput superconductor discovery via machine learning
Stephen R. Xie, Y. Quan, Ajinkya Hire +5
Even though superconductivity has been studied intensively for more than a century, the vast majority of superconductivity research today is carried out in nearly the same manner a…