Strategies to break linear scaling relationships

399 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A foundation model for atomistic materials chemistry. (2023, neutral): “MACE-MP-0+D3 captures these trends well, and the slopes of the linear fits are in reasonable agreement with DFT ( e.g. 0 .”
Machine learning for a sustainable energy future (2022, background): “The most common descriptor of catalytic activity is the adsorption energy of intermediates on a catalyst 61,62 .”
High-entropy materials for catalysis: A new frontier (2021, background): “The pronounced cooperative effects on the HEA surface could potentially break the scaling relationships for electrocatalysis, surpassing the long-established not-too-strong, not-too-weak guidance (68).”
High-entropy nanoparticles: Synthesis-structure-property relationships and data-driven discovery (2022, background): “In catalysis, the binding of reactants or intermediates to the catalyst surface should be neither too strong nor too weak (the Sabatier principle) to maximize the performance, thus showing a “volcano plot” in the dependence of activity on binding energy (83, 84).”