Researchers from AMBER, the SFI Research Centre for Advanced Materials and BioEngineering Research, the School of Physics and the CRANN Institute, at Trinity College Dublin, have developed a new method to model the atomic world.
Find out what happens within chemical and biochemical reactions
The research will enable fast and efficient ways for scientists to find out what happens within chemical and biochemical reactions.
This new method could prove very useful to model experiments for the aerospace industry where it is difficult and costly to identify and test prototype materials that maintain their properties under very high pressure and temperature.
Using an accurate and efficient model could act as a precursor to identify better, more robust, materials before physical construction and testing.
All materials, including living beings, are made of atoms - the smallest building blocks of the material world.
Many models currently exist that can predict what will happen when molecules form covalent bonds – which is a bond that forms when different atoms share electrons.
In order to model what will happen when covalent bonds are formed, scientists use the fundamental equations of quantum mechanics, the Schrodinger’s equation. However this generally requires significant computing power and can take a considerable amount of time to complete.
Bypassing traditional way of modelling the atomic world
AMBER researchers have made a significant contribution to the field bypassing this traditional way of modelling the atomic world.
Researchers have taught a computer the underlying physics and chemistry associated with a covalent bond. Using machine-learning methodologies this has enabled the research team to make a breakthrough in modelling – meaning that, through artificial intelligence, computers used to model materials can learn by themselves by reviewing the available data.
As Dr Alessandro Lunghi, postdoctorate researcher of the School of Physics and CRANN, explains: “in a sense, our models learnt the chemistry of the chemical bond just by looking at the reference molecular configurations we provided.”
Using machine learning will make a significant advance in materials science according to lead investigator on the study, Professor Stefano Sanvito, professor in the School of Physics and director of the CRANN Institute, Trinity College Dublin: “There are a range of numerical techniques, called first principles methods that scientists traditionally use to simulate how materials behave at the atomic level.
"These require us to solve the fundamental equation of quantum mechanics (the Schrodinger equation). While these simulations are usually highly accurate, they need lot of computational resources to complete. In our work we have constructed a range of models that avoid the need for solving the Schrodinger equation, but achieve an identical level of accuracy.
"Using machine learning, which is a branch of artificial intelligence research, it allows us to simulate any material at the atomic level in a shorter amount of time than traditional methods.
'As accurate as computationally expensive first principles approach'
"We have invented a novel way to systematically construct atomistic models for materials, which are as accurate as the computationally expensive first principles approach.”
The study is published in 'Science Advances'* a leading international science journal. The study was led by AMBER researchers at the School of Physics and CRANN Institute, Trinity.
Professor Mick Morris, director of AMBER and professor in Trinity’s School of Chemistry, said: “This is another example of the fundamental research which underpins AMBER’s work and uncovers new ways to understand the world around us, in this case at the smallest of scales.
"This research will enable materials scientists to see into the atomic world and further push the boundaries of science to discover real solutions that can improve people’s lives. I wish to congratulate Stefano and his team on this exciting development and its publication in 'Science Advances'.”
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https://advances.sciencemag.org/content/5/5/eaaw2210.abstract