Method Development

3 min read

Our predictions about materials are only as reliable as the methods behind them. Alongside our work on specific materials, we develop and test methods that make first-principles simulations more accurate, reach properties that standard approaches miss, and extend them to the larger systems and longer times over which many properties of real materials emerge. Where we can, we release them as open-source codes, listed on our Software page.

More accurate density functionals. Density functional theory struggles with transition-metal oxides, whose partly filled d shells make the self-interaction error of common functionals severe, which in turn degrades band gaps, magnetic moments and oxidation energies, all of which matter for battery cathodes. With r²SCANY@r²SCANX, we use one fraction of exact exchange to compute the electron density and another for the energy, correcting both sources of error at once. With only one or two parameters, it improves on r²SCAN for 20 strongly correlated oxides and outperforms the widely used r²SCAN+U.^[1]

Realistic surface models. Cutting a crystal of a multinary compound along a conventional plane often slices through its strongly bonded polyhedra and gives unphysical surface energies. Our code SALAMI builds symmetric, charge-neutral and dipole-free slab models whose surface atoms keep their preferred coordination. For the solid electrolyte Li₃PS₄ and the transparent conducting oxide ZnSb₂O₆, these models lower the surface energies markedly and shrink the predicted Wulff shape by about 20%.^[2]

Ion transport over long times. Kinetic Monte Carlo can follow ion migration for far longer than molecular dynamics, but building a model of the migration rates has been laborious. Our Python package kMCpy enumerates all possible migration events in a crystal, derives their rates at first-principles accuracy through cluster-expansion Hamiltonians, and solves the kinetics, for materials of any dimensionality.^[3]

Larger systems with machine learning. Machine-learning interatomic potentials, trained on first-principles calculations, reproduce their accuracy at a small fraction of the cost and make simulations of thousands of atoms over nanoseconds routine. With colleagues across the field, we have reviewed what these potentials can and cannot yet do for battery research, and what the next generation will need.^[4]

Relevant references