Professor Chris Pickard, Fellow is a materials scientist who employs first principles methods where research is now accelerated by advances in machine learning.
He said:
“It’s a gold rush moment, where everyone can do things that no one’s done before.”
First principles methods are computational techniques that predict material properties directly from the fundamental physical laws such as quantum mechanics and Newton’s Laws without relying on experimental data or empirical parameters.
Computer code can be used for ‘structure prediction’ which provides thousands of different arrangements of atoms to help understand the material properties of a particular compound.
One of Professor Pickard’s early papers mapped out the phases of hydrogen at high pressure. Before this work, scientists had only been able to speculate as to what these phases might look like.
Now with machine learning, the field is developing at speed. While the basis is still one of the most important equations in Physics: the Schrödinger equation, the calculations can be performed up to 100,000 times faster.
Professor Pickard said:
“This breakthrough in machine learning is making a lot of things that I thought I would never do in my career move forward 10 or 20 years”
His work at the Department of Materials Science and Metallurgy includes using these computational methods to discover new materials for batteries and working closely with experimentalists to search for high-temperature superconductors based on hydrogen.
He said:
“I’d say the ideal situation is to do the right amount of computing and the right amount of experiments to get to the answer that you’re looking for as quickly as possible.”
Read a full Q&A with Professor Pickard in American Scientist and listen to a podcast ‘First Principles and Beyond’.