The award goes to research contributions that highlights what the RCS defines as "the most exciting, contemporary chemical science at the cutting edge of research" and for teams or collaborations that opens up new directions or possibilities in their field.
SINTEF, alongside The University of Manchester and partners in Russia, Italy, and Australia, have received this award for developing a tool that simulates and predicts crystal growth. The tool can tell us something about the properties, purity, and performance of different materials and enable us to develop better materials for various applications.
The name of the software is CrystalGrower and is based on so-called Monte Carlo simulations. Monte Carlo simulation is a mathematical technique for estimating the possible outcomes of an uncertain event or parameters. With this tool anyone can grow any crystal virtually on their laptop, with both crystal habits and nanoscopic surface topography of any crystal structure being simulated.
– We have an extremely hard-working team that has maintained passion for a project that we know can make a huge impact on the way chemicals are used to better multiple international industries, says Professor Mike Anderson from The University of Manchester.
Crystals are found all around us. They can appear as salts or sugars, diamonds and pharmaceuticals or solar cells and computer chips. The understanding and controlling of crystals will make us able to develop advanced materials for specific applications, while saving both time and money.
– What is important is that it can simulate crystal growth at the atomic level. With CrystalGrower it is possible to analyze the inside of the crystal to see if it is perfect or if it has lots of "flaws". This will be crucial when material is produced from such crystals for various applications, says Duncan Akporiaye, research director in SINTEF.
CrystalGrower: a generic computer program for Monte Carlo modelling of crystal growth, Chem. Sci., 2021,12, 1126-1146
Predicting crystal growth via a unified kinetic three-dimensional partition model. Nature 544, 456–459 (2017).