Computational design of organic molecules for reducing friction at the nanoscale

Computational modeling has the promise to enable atom-by-atom design of nanoscale properties that give rise to essential changes in macroscale properties. In the quest for increasing energy efficiency and resource utilization, energy losses remain an outstanding challenge that can be solved in part through computational materials design. Friction reducers (FRs) are molecular additives that can minimize friction loss in these engines by reducing friction between moving parts at the nanoscale. Traditional FRs contain metals, sulfur, and phosphorus, which can poison exhaust system catalysts and diesel particulate filters. Thus, if suitably designed, organic friction reducers (OFRs) present a promising alternative solution. Here, we apply non-equilibrium molecular dynamics simulations together with density functional theory methods to enable the atom-by-atom design of OFRs. We directly compute the coefficients of friction of OFRs on model engine iron oxide surfaces with varying coverage and temperature, and explore a number of conditions not easily probed during experiments. These studies allow us to build a quantitative structural property relationship for predicting good OFR characteristics, enabling an iteratively improving materials design workflow.