Neural networks predict forces in jammed granular solids

Granular matter is all around us. Examples include sand, rice, nuts, coffee and even snow. These materials are made of solid particles that are large enough not to experience thermal fluctuations. Instead, their state is determined by mechanical influences: shaking produces ‘granular gases’ whilst by compression one gets ‘granular solids’. An unusual feature of such solids is that forces within the material concentrate along essentially linear paths called force chains whose shape resembles that of lightning. Apart from granular solids, other complex solids such as dense emulsions, foams and even groups of cells can exhibit these force chains. Researchers used machine learning and computer simulations to predict the position of force chains.