Nav: Home

Machine learning predicts mechanical properties of porous materials

May 15, 2019

Machine learning can be used to predict the properties of a group of materials which, according to some, could be as important to the 21st century as plastics were to the 20th.

Researchers have used machine learning techniques to accurately predict the mechanical properties of metal organic frameworks (MOFs), which could be used to extract water from the air in the desert, store dangerous gases or power hydrogen-based cars.

The researchers, led by the University of Cambridge, used their machine learning algorithm to predict the properties of more than 3000 existing MOFs, as well as MOFs which are yet to be synthesised in the laboratory.

The results, published in the inaugural edition of the Cell Press journal Matter, could be used to significantly speed up the way materials are characterised and designed at the molecular scale.

MOFs are self-assembling 3D compounds made of metallic and organic atoms connected together. Like plastics, they are highly versatile, and can be customised into millions of different combinations. Unlike plastics, which are based on long chains of polymers that grow in only one direction, MOFs have orderly crystalline structures that grow in all directions.

This crystalline structure means that MOFs can be made like building blocks: individual atoms or molecules can be switched in or out of the structure, a level of precision that is impossible to achieve with plastics.

The structures are highly porous with massive surface area: a MOF the size of a sugar cube laid flat would cover an area the size of six football fields. Perhaps somewhat counterintuitively however, MOFs make highly effective storage devices. The pores in any given MOF can be customised to form a perfectly-shaped storage pocket for different molecules, just by changing the building blocks.

"That MOFs are so porous makes them highly adaptable for all kinds of different applications, but at the same time their porous nature makes them highly fragile," said Dr David Fairen-Jimenez from Cambridge's Department of Chemical Engineering and Biotechnology, who led the research.

MOFs are synthesised in powder form, but in order to be of any practical use, the powder is put under pressure and formed into larger, shaped pellets. Due to their porosity, many MOFs are crushed in this process, wasting both time and money.

To address this problem, Fairen-Jimenez and his collaborators from Belgium and the US developed a machine learning algorithm to predict the mechanical properties of thousands of MOFs, so that only those with the necessary mechanical stability are manufactured.

The researchers used a multi-level computational approach in order to build an interactive map of the structural and mechanical landscape of MOFs. First, they used high-throughput molecular simulations for 3,385 MOFs. Secondly, they developed a freely-available machine learning algorithm to automatically predict the mechanical properties of existing and yet-to-be-synthesised MOFs.

"We are now able to explain the landscape for all the materials at the same time," said Fairen-Jimenez. "This way, we can predict what the best material would be for a given task."

The researchers have launched an interactive website where scientists can design and predict the performance of their own MOFs. Fairen-Jimenez says that the tool will help to close the gap between experimentalists and computationalists working in this area. "It allows researchers to access the tools they need in order to work with these materials: it simplifies the questions they need to ask," he said.
-end-
The research was funded in part by the Royal Society and the European Research Council.

University of Cambridge

Related Molecules Articles:

How molecules self-assemble into superstructures
Most technical functional units are built bit by bit according to a well-designed construction plan.
Breaking down stubborn molecules
Seawater is more than just saltwater. The ocean is a veritable soup of chemicals.
Shaping the rings of molecules
Canadian chemists discover a natural process to control the shape of 'macrocycles,' molecules of large rings of atoms, for use in pharmaceuticals and electronics.
The mysterious movement of water molecules
Water is all around us and essential for life. Nevertheless, research into its behaviour at the atomic level -- above all how it interacts with surfaces -- is thin on the ground.
Spectroscopy: A fine sense for molecules
Scientists at the Laboratory for Attosecond Physics have developed a unique laser technology for the analysis of the molecular composition of biological samples.
Looking at the good vibes of molecules
Label-free dynamic detection of biomolecules is a major challenge in live-cell microscopy.
Colliding molecules and antiparticles
A study by Marcos Barp and Felipe Arretche from Brazil published in EPJ D shows a model of the interaction between positrons and simple molecules that is in good agreement with experimental results.
Discovery of periodic tables for molecules
Scientists at Tokyo Institute of Technology (Tokyo Tech) develop tables similar to the periodic table of elements but for molecules.
New method for imaging biological molecules
Researchers at Karolinska Institutet in Sweden have, together with colleagues from Aalto University in Finland, developed a new method for creating images of molecules in cells or tissue samples.
How two water molecules dance together
Researchers have gained new insights into how water molecules interact.
More Molecules News and Molecules Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Teaching For Better Humans 2.0
More than test scores or good grades–what do kids need for the future? This hour, TED speakers explore how to help children grow into better humans, both during and after this time of crisis. Guests include educators Richard Culatta and Liz Kleinrock, psychologist Thomas Curran, and writer Jacqueline Woodson.
Now Playing: Science for the People

#556 The Power of Friendship
It's 2020 and times are tough. Maybe some of us are learning about social distancing the hard way. Maybe we just are all a little anxious. No matter what, we could probably use a friend. But what is a friend, exactly? And why do we need them so much? This week host Bethany Brookshire speaks with Lydia Denworth, author of the new book "Friendship: The Evolution, Biology, and Extraordinary Power of Life's Fundamental Bond". This episode is hosted by Bethany Brookshire, science writer from Science News.
Now Playing: Radiolab

Space
One of the most consistent questions we get at the show is from parents who want to know which episodes are kid-friendly and which aren't. So today, we're releasing a separate feed, Radiolab for Kids. To kick it off, we're rerunning an all-time favorite episode: Space. In the 60's, space exploration was an American obsession. This hour, we chart the path from romance to increasing cynicism. We begin with Ann Druyan, widow of Carl Sagan, with a story about the Voyager expedition, true love, and a golden record that travels through space. And astrophysicist Neil de Grasse Tyson explains the Coepernican Principle, and just how insignificant we are. Support Radiolab today at Radiolab.org/donate.