A new dawn of interatomic potentials

I will show our recent work on data driven interatomic potentials.

The goal of this research programme is to construct analytic functions that accurately reproduce the Born-Oppenheimer potential energy surface of condensed phase materials. Much progress has been made by an increasing number of groups over the last few years, mostly by borrowing approaches and attitudes from the field of machine learning - even though the mathematical context is rather different.

Accurate potentials have been published by us for carbon, silicon, boron, tungsten, iron, that cover a wide range of atomic environments, and for many other materials by other groups. These potentials are beginning to be used in materials science applications.


[1] Bartók, AP and De, S and Poelking, C and Bernstein, N and Kermode, JR and Csányi, G and Ceriotti, M (2017) Machine learning unifies the modeling of materials and molecules. Sci Adv, 3. e1701816
[2] Deringer, VL and Csányi, G (2017) Machine learning based interatomic potential for amorphous carbon. Physical Review B, 95. ISSN 2469-9950
[3] Dragoni, D and Daff, TD and Csanyi, G and Marzari, N (2018) Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron. PHYSICAL REVIEW MATERIALS, 2. ISSN 2475-9953
[4] Deringer, VL and Pickard, CJ and Csányi, G (2018) Data-Driven Learning of Total and Local Energies in Elemental Boron. Phys Rev Lett, 120. 156001
[5] Caro, MA and Deringer, VL and Koskinen, J and Laurila, T and Csányi, G (2018) Growth Mechanism and Origin of High sp^{3} Content in Tetrahedral Amorphous Carbon. Phys Rev Lett, 120. 166101