Materials surround us—from raw materials such as metal ores in the earth, protein biomaterials in our bodies to functional materials that power electronic devices. Science and engineering demands the utmost of materials, and increasingly their properties are being tested quickly and efficiently using computer modelling1.
Modelling materials-related processes is not simple, as the underlying phenomena span an enormous range of lengths and timescales2,3. For example, although metal corrosion is initiated by electron movements that occur within trillionths of a second, it takes minutes for the first surface layers to form rust and it can take years or centuries for the destructive effects to become significant. The levels of information in computational materials science are generally classified into four regimes. First, at the quantum scale: theoretical calculations and practical experiments describe how electrons behave in atoms, exposing the nature of chemical bonding from which material properties derive; such detailed computations are limited to relatively small groups of several hundred atoms, which are now detailing the behaviour of electrons at the attosecond3,4,11. Second, at the atomistic level: molecular-dynamic calculations simulate movements of millions of atoms and molecules according to known electromagnetic principles; movement can be simulated only for brief periods (billionths of a second). Third, at the mesoscopic scale, computations replace thousands of atoms with an average property, such as mass density, charge or temperature, allowing physical simulations to proceed for longer (from nanoseconds to microseconds). Fourth, at the macroscopic scale: millions of particles are treated as a continuous distribution, and physical properties are solved using classical thermodynamic and kinetic equations. This allows researchers to simulate processes, such as flow through a pipeline or the efficiency of a turbine, in real time.
Connecting these levels is vital for developing multi-scale models that describe and even predict essential materials behaviour5,6. This paper provides three examples of progress towards this goal through developing quantitative design strategies, discovering important natural mechanisms, and using quantum mechanics to predict products and processes.
Proteins comprise amino-acid chains that fold into unique shapes within cells. This folding process is central to a protein’s biological function — for example, Alzheimer’s disease is characterized by misfolded amyloid-protein, which agglomerates into plaques. Establishing accurate models of protein structures could lead to breakthroughs in therapeutic treatments.