Researchers from the Japan Advanced Institute of Science and Technology have solved a previously perplexing materials science problem with a supercomputer and quantum simulations.
Comprehending the structural properties of molecules in nature or synthesised in laboratory is fundamental to materials science. As science and technology have developed over the years, this challenge has also evolved, with scientists aiming to find new materials with more attractive properties. In order to undertake this task methodically, scientists depend on complex simulation methods that integrate the rules of quantum mechanics, which are also the rules that govern the molecules.
Utilising quantum simulations
This quantum simulation-based approach has been incredibly effective, to the point where an entire field of study, known as materials informatics, has been devoted to it. However, challenges still remain.
One such challenge is disiloxane, which is a silicon (Si)-containing compound consisting of a Si-O-Si bridge with three hydrogen atoms at each end. The structure is relatively straightforward, but scientists have struggled to estimate how much energy is needed to bend the Si-O-Si bridge. So far, experimental results have been unreliable and theoretical calculations have produced different values due to the sensitivity of the calculated properties to parameter choices and level of theory.
Now, an international team led by Dr. Kenta Hongo, Associate Professor at the Japan Advanced Institute of Science and Technology, has successfully solved this dilemma.
The Monte Carlo method
The researchers were able to confront this challenge by utilising an advanced simulation method known as ‘first-principles quantum Monte Carlo method’ that finally overcame the obstacles other methods could not conquer.
The team’s findings have been published in Physical Chemistry Chemical Physics.
However, the researchers stressed that overcoming this challenge did not merely come down to developing better simulations: “Getting an answer that does not agree with the experimentally known value is, in itself, not surprising. The agreement can improve with more careful, and more expensive, simulations. But with disiloxane, the agreement actually becomes worse with more careful simulations,” explained Dr. Hongo.
“What our method has achieved, rather, is good results without much dependence on the adjustment parameters, so that we don’t need to worry about whether the adjusted values are sufficient.”
The scientists compared the first-principles quantum Monte Carlo method with other standard methods, including ‘density functional theory’ (DFT) calculations and ‘coupled cluster method with single and double substitutions and noniterative triples’ (CCSD(T)), alongside empirical measurements from past studies. The three techniques mainly varied in their sensitivity towards the ‘completeness’ of basis sets, which is a set of functions employed to define the quantum wavefunctions.
Tweaking the amplitude
The team founddiscovered that for DFT and CCSD(T), the choice of basis set impacted the amplitude as well as positions of zero amplitude for the wavefunctions, while for quantum Monte Carlo, it only affected the zero amplitude positions. This permitted the researchers to tweak the amplitude in a way that the wavefunction shape approached that of an exact solution.
“This self-healing property of the amplitude works well to reduce the basis-set dependence and lower the bias arising from an incomplete basis set in calculating the bending energy barrier,” added Dr Hongo.
While this is an exciting development, Professor Hongo highlighted the wider implications of the discovery: “Molecular simulations are widely used to design new medicines and catalysts. Getting rid of the fundamental difficulties in using them greatly contributes to the design of such materials. With our powerful supercomputers, the method used in our study could be a standard strategy for overcoming such difficulties,” he concluded.