Crystal structure representations for machine learning models of formation energies
By Felix A. Faber, Alexander Hans Gustav Lindmaa, O. V. Lilienfeld, Rickard Armiento. 2015
421 citations recorded by the source corpus.
We introduce and evaluate a set of feature vector representations of crystal structures for machine learning (ML) models of formation energies of solids. ML models of atomization energies of organi ...
Subfields: Computational Theory and Mathematics, Materials Chemistry
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Florian Hase
- Pascal Friederich
- Alexander V. Shapeev
- Jonny Proppe
- Evgeny V. Podryabinkin
- Alán Aspuru-Guzik
- Alok N. Choudhary
- Chris Wolverton
In Materials Chemistry
- Evgeny V. Podryabinkin
- Logan T. Ward
- Alexander V. Shapeev
- Alok N. Choudhary
- Chris Wolverton
- Florian Hase
- Pascal Friederich
- Ankit Agrawal
Citations in the corpus
- Learning atoms for materials discovery (2018, background): “The input or feature of materials involves descriptors of constituents: Certain physical or chemical attributes of atoms are taken, depending on the materials property under prediction (11, 14, 17).”
- A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials (2016, positive): “14,17 As an example, the volume of a crystalline compound is expected to relate to the volume of the constituent elements.”
- Active learning of linearly parametrized interatomic potentials (2016, background): “It should be noted that there are other, ML-based atomistic models of solids, including those predicting the energy directly without partitioning it [19, 20], or constructing a density functional in a DFT with machine learning [21].”
- Machine-learned potentials for next-generation matter simulations (2021, background): “Further constraints on the representation can be posed by additional symmetries of the system such as periodicity in the case of crystalline materials 46,47 .”
- A perspective on conventional high-temperature superconductors at high pressure: Methods and materials (2019, background): “ speed-up the searches by learning inter-atomic model potentials [370, 371]. In principle, the construction of a generalized convex hull is a type of problem perfectly accessible for machine learning [372] and neural networks [373]. Forces are relatively straightforward to machine-learn and in the c”