Lightweight, high-strength aluminum alloys are essential for making cars and planes more fuel-efficient.
But despite their promise, these materials are difficult to manufacture consistently, especially at the scale needed for the automotive industry.
Now, a research team led by the University of Michigan, working with General Motors Research & Development, has developed a new computer model that could change that.
The team created a fast and efficient way to predict how aluminum alloys become strong as they cool and age.
Their model helps engineers fine-tune how these metals are processed, opening the door to cheaper, more reliable production of lightweight aluminum parts for vehicles.
The study was published in npj Computational Materials.
The researchers focused on a family of materials known as 7000-series aluminum alloys, which contain magnesium and zinc.
These alloys are already widely used in aerospace because they are extremely strong for their weight.
Their strength comes from tiny clusters of magnesium and zinc atoms that form inside the aluminum and act like reinforcements.
However, these alloys have been difficult to use in cars. One major challenge is a process called natural aging, which happens when the metal sits at room temperature after being cooled. During this time, the metal’s strength can change unpredictably.
To avoid this, aerospace manufacturers rely on expensive processing techniques, such as extreme temperature control, that are impractical for large-scale car manufacturing.
To solve this problem, the researchers wanted to better understand what happens inside the metal at the smallest scale during cooling and aging. When aluminum alloys are heat-treated, they are first heated to very high temperatures so magnesium and zinc dissolve evenly.
The metal is then rapidly cooled, or quenched. This sudden cooling traps tiny defects called vacancies—missing atoms in the crystal structure.
These vacancies play a key role. They help magnesium and zinc atoms move around and form strengthening clusters. How many vacancies remain, and where they end up, depends strongly on how fast the metal is cooled.
Simulating this process directly is extremely difficult. Atomic-scale movements happen over days, and traditional computer methods can only handle seconds of real time. To overcome this, the team developed a multiscale model that connects atomic behavior to long-term changes in the metal.
Instead of tracking every atom, the new approach treats vacancy motion like a guided random walk through an energy landscape. This information is then fed into a higher-level model that predicts how clusters grow and change over hours or days. With this method, simulations that once took enormous computing power can now be run in minutes.
The model revealed that cooling speed leaves behind a lasting “vacancy fingerprint.” Fast cooling keeps more vacancies mobile, speeding up aging and strengthening. Slower cooling allows larger clusters to form early, trapping vacancies and slowing the aging process.
By predicting how chemistry, cooling rate, and aging time interact, the new model reduces the need for costly trial-and-error experiments.
In practical terms, it gives engineers a powerful tool to design stronger, lighter aluminum alloys that are easier and cheaper to manufacture—bringing advanced materials one step closer to everyday cars.
