The Hiphive Package for the Extraction of High‐Order Force Constants by Machine Learning
By F. Eriksson, Erik Fransson, Paul Erhart. Advanced Theory and Simulations, 2018
401 citations recorded by the source corpus.
The efficient extraction of force constants (FCs) is crucial for the analysis of many thermodynamic materials properties. Approaches based on the systematic enumeration of finite differences scale poorly with system size and can rarely extend beyond third order when input data is obtained from first‐principles calculations. Methods based on parameter fitting in the spirit of interatomic potentials, on the other hand, can extract FC parameters from semi‐random configurations of high information density and advanced regularized regression methods can recover physical solutions from a limited amount of data. Here, the hiphive Python package, that enables the construction of force constant models up to arbitrary order is presented. hiphive exploits crystal symmetries to reduce the number of free parameters and then employs advanced machine learning algorithms to extract the force constants. Depending on the problem at hand, both over and underdetermined systems are handled efficiently. The FCs can be subsequently analyzed directly and or be used to carry out, for example, molecular dynamics simulations. The utility of this approach is demonstrated via several examples including ideal and defective monolayers of MoS2 as well as bulk nickel.
Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/5c152144a91addc391f33b44d22c6f443f502a9b
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In Atomic and Molecular Physics, and Optics
In Materials Chemistry
- Michele Simoncelli
- Nicola Marzari
- F. Mauri
- T. Ala‐Nissila
- Haikuan Dong
- Zezhu Zeng
- Zheyong Fan
- Cunzhi Zhang
Citations in the corpus
- Wigner Formulation of Thermal Transport in Solids (2021, methodology): “6 have been reproduced using the software phono3py [6] and the tetrahedron method (the softwares phonopy [201] and hiphive [5] have been used to convert force constants from Quantum espresso or ShengBTE format to the hdf5 format used by phono3py).”
- Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport (2021, methodology): “We also added 20 cells with small random atom displacements generated by using the hiphive package [56], to sample the low-temperature phase space of PbTe.”