Why is it that ABO 3 perovskites generally do not exhibit negative thermal expansion (NTE) over a wide temperature range, whereas layered perovskites of the same chemical family often do? It is generally accepted that there are two key ingredients that determine the extent of NTE: the presence of soft phonon modes that drive contraction (have negative Grüneisen parameters); and anisotropic elastic compliance that predisposes the material to the deformations required for NTE along a specific axis. This difference in thermal expansion properties is surprising since both ABO 3 and layered perovskites often possess these ingredients in equal measure in their high-symmetry phases. Using first principles calculations and symmetry analysis, we show that in layered perovskites there is a significant enhancement of elastic anisotropy due to symmetry breaking that results from the combined effect of layering and condensed rotations of oxygen octahedra. This feature, unique to layered perovskites of certain symmetry, is what allows uniaxial NTE to persist over a large temperature range. This fundamental insight means that symmetry and the elastic tensor can be used as descriptors in high-throughput screening and to direct materials design.
The layered perovskite Ca3-xSrxMn2O7 is shown to exhibit a switching from a material exhibiting uniaxial negative to positive thermal expansion as a function of x. The switching is shown to be related to two closely competing phases with different symmetries. The negative thermal expansion (NTE) effect is maximized when the solid solution is tuned closest to this region of phase space but is switched off suddenly on passing though the transition. Our results show for the first time that, by understanding the symmetry of the competing phases alone, one may achieve unprecedented chemical control of this unusual property.
Neural network force field (NNFF) is a method for performing regression on atomic structureforce relationships, bypassing expensive quantum mechanics calculation which prevents the execution of long ab-initio quality molecular dynamics simulations. However, most NNFF methods for complex multielement atomic systems indirectly predict atomic force vectors by exploiting just atomic structure rotation-invariant features and the network-feature spatial derivatives which are computationally expensive. We develop a staggered NNFF architecture exploiting both rotation-invariant and covariant features separately to directly predict atomic force vectors without using spatial derivatives, thereby reducing expensive structural feature calculation by ~180-480×. This acceleration enables us to develop NNFF which directly predicts atomic forces in complex ternary and quaternary-element extended systems comprised of long polymer chains, amorphous oxide, and surface chemical reactions. The staggered rotation-invariant-covariant architecture described here can also directly predict complex covariant vector outputs from local physical structures in domains beyond computational material science.
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