The MLIP package: moment tensor potentials with MPI and active learning
By Ivan S. Novikov, Konstantin Gubaev, Evgeny V. Podryabinkin, Alexander V. Shapeev. Machine Learning: Science and Technology, 2020
643 citations recorded by the source corpus.
The subject of this paper is the technology (the ‘how’) of constructing machine-learning interatomic potentials, rather than science (the ‘what’ and ‘why’) of atomistic simulations using machine-learning potentials. Namely, we illustrate how to construct moment tensor potentials using active learning as implemented in the MLIP package, focusing on the efficient ways to automatically sample configurations for the training set, how expanding the training set changes the error of predictions, how to set up ab initio calculations in a cost-effective manner, etc. The MLIP package (short for Machine-Learning Interatomic Potentials) is available at https://mlip.skoltech.ru/download/.
Subfields: Materials Chemistry, Surfaces, Coatings and Films
View Paper: https://www.semanticscholar.org/paper/d7009d10dd80d556ac28becad1e035c7cc2cde90
How researchers see this paper
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In Materials Chemistry
- Xiaoying Zhuang
- Timon Rabczuk
- Bohayra Mortazavi
- F. Shojaei
- B. Javvaji
- T. Ala‐Nissila
- Haikuan Dong
- Zezhu Zeng
In Surfaces, Coatings and Films
Citations in the corpus
- Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport (2021, methodology): “…a few case studies to evaluate the performance of NEP implemented in gpumd , as compared to the quip [15] package that implements the GAP-SOAP potential [7, 30], the mlip package [16] that implements the MTP potential [17], and the DeePMD-kit package [18] that implements the DP potential [19, 20].”
- Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles (2020, methodology): “MTPs are then trained passively with the same procedure explained in our earlier studies [62,63] using the MLIP package [64].”