Worldwide, industries face significant challenges that often require the discovery and design of new materials spanning sustainable energy, biotechnology or lightweight materials for transport applications — but materials development is slow and costly. For example, the process of drug discovery today can take an average of 12 to 15 years, with billions of dollars invested per drug and a 90 percent fallout rate.
The ability to predict the properties of materials from those of their molecular building blocks is therefore a challenge which impacts the biomedical, engineering, and chemical sectors and involves both big data analytics and model-driven strategies. Advances in computational power mean larger and more complex materials be simulated at the molecular scale. It is now possible to use high performance computers to calculate target properties of hundreds of thousand of materials, store them in databases and use them for prediction of novel materials. But materials yet to be discovered require powerful simulation tools which can predict properties in advance of their synthesis or under conditions such as high temperatures or pressures which are challenging to replicate experimentally.
Computer power alone is not enough to deliver on this challenge and fundamentally new strategies which offer improved predictive power without vastly increased computational cost are needed.
For more than five years, IBM Research
has developed a new strategy for materials simulation, in collaboration with us here at the Hartree Centre.
Read the full strategy here
, published in the Reviews of Modern Physics.
Find out more about this ongoing area of work in an IBM Research blog post
from Flaviu Cipcigan and Jason Crain.
F. S. Cipcigan, J. Crain, V. P. Sokhan, and G. J. Martyna. Rev. Mod. Phys. 91, 025003 (2019)