Artificial Intelligence Helps in the Discovery of New Materials

The matrix depicts the formation energy – an indicator of stability – of around two million possible compounds. Each pixel corresponds to one of the two million quaternary crystals. Depending on the combination of elements, they display either a high (red) or low (blue) energy value. One element is specified vertically and one horizontally; each box contains a suitable resolution for the two remaining elements. (Image: University of Basel, Department of Chemistry)

The matrix depicts the formation energy – an indicator of stability – of around two million possible compounds. Each pixel corresponds to one of the two million quaternary crystals. Depending on the combination of elements, they display either a high (red) or low (blue) energy value. One element is specified vertically and one horizontally; each box contains a suitable resolution for the two remaining elements. (Image: University of Basel, Department of Chemistry)

With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. They report on their findings in the scientific journal Physical Review Letters.

Elpasolite is a glassy, transparent, shiny and soft mineral with a cubic crystal structure. First discovered in El Paso County (Colorado, USA), it can also be found in the Rocky Mountains, Virginia and the Apennines (Italy). In experimental databases, elpasolite is one of the most frequently found quaternary crystals (crystals made up of four chemical elements). Depending on its composition, it can be a metallic conductor, a semi-conductor or an insulator, and may also emit light when exposed to radiation.

These characteristics make elpasolite an interesting candidate for use in scintillators (certain aspects of which can already be demonstrated) and other applications. Its chemical complexity means that, mathematically speaking, it is practically impossible to use quantum mechanics to predict every theoretically viable combination of the four elements in the structure of elpasolite.

Machine learning aids statistical analysis

Thanks to modern artificial intelligence, Felix Faber, a doctoral student in Prof. Anatole von Lilienfeld’s group at the University of Basel’s Department of Chemistry, has now succeeded in solving this material design problem. First, using quantum mechanics, he generated predictions for thousands of elpasolite crystals with randomly determined chemical compositions. He then used the results to train statistical machine learning models (ML models). The improved algorithmic strategy achieved a predictive accuracy equivalent to that of standard quantum mechanical approaches.

ML models have the advantage of being several orders of magnitude quicker than corresponding quantum mechanical calculations. Within a day, the ML model was able to predict the formation energy – an indicator of chemical stability – of all two million elpasolite crystals that theoretically can be obtained from the main group elements of the periodic table. In contrast, performance of the calculations by quantum mechanical means would have taken a supercomputer more than 20 million hours.

Unknown materials with interesting characteristics

An analysis of the characteristics computed by the model offers new insights into this class of materials. The researchers were able to detect basic trends in formation energy and identify 90 previously unknown crystals that should be thermodynamically stable, according to quantum mechanical predictions.

On the basis of these potential characteristics, elpasolite has been entered into the Materials Project material database, which plays a key role in the Materials Genome Initiative. The initiative was launched by the US government in 2011 with the aim of using computational support to accelerate the discovery and the experimental synthesis of interesting new materials.

Some of the newly discovered elpasolite crystals display exotic electronic characteristics and unusual compositions. “The combination of artificial intelligence, big data, quantum mechanics and supercomputing opens up promising new avenues for deepening our understanding of materials and discovering new ones that we would not consider if we relied solely on human intuition,” says study director von Lilienfeld.

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Bringing artificial enzymes closer to nature

Representation of the new-to-nature olefin metathesis reaction in E. coli using a ruthenium-based artificial metalloenzyme to produce novel high added-value chemicals. (Image: NCCR Molecular Systems Engineering)

Representation of the new-to-nature olefin metathesis reaction in E. coli using a ruthenium-based artificial metalloenzyme to produce novel high added-value chemicals. (Image: NCCR Molecular Systems Engineering)

Scientists at the University of Basel, ETH Zurich, and NCCR Molecular Systems Engineering have developed an artificial metalloenzyme that catalyses a reaction inside of cells without equivalent in nature. This could be a prime example for creating new non-natural metabolic pathways inside living cells, as reported today in Nature.

The artificial metalloenzyme, termed biot-Ru–SAV, was created using the biotin–streptavidin technology. This method relies on the high affinity of the protein streptavidin for the vitamin biotin, where compounds bound to biotin can be introduced into the protein to generate artificial enzymes. In this study the authors introduced an organometallic compound, with the metal ruthenium at its base. Organometallic compounds are molecules containing at least one bond between a metal and a carbon atom, and are often used as catalysts in industrial chemical reactions. However, organometallic catalysts perform poorly, if at all, in aqueous solutions or cellular-like environments, and need to be incorporated into protein scaffolds like streptavidin to overcome these limitations.

“The goal was to create an artificial metalloenzyme that can catalyse olefin metathesis, a reaction mechanism that is not present among natural enzymes,” says Thomas R Ward, Professor at the Department of Chemistry, University of Basel, and senior author of the study. The olefin metathesis reaction is a method for the formation and redistribution of carbon-carbon double bonds widely used in laboratory research and large-scale industrial productions of various chemical products. Biot-Ru–SAV catalyses a ring-closing metathesis to produce a fluorescent molecule for easy detection and quantification.

Periplasm as reaction compartment

However, the environment inside a living cell is far from ideal for the proper functioning of organometallic-based enzymes. “The main breakthrough was the idea to use the periplasm of Escherichia coli as a reaction compartment, whose environment is much better suited for an olefin metathesis catalyst,” says Markus Jeschek, a researcher from the team of co-supervising author Sven Panke at the Department of Biosystems Science and Engineering, ETH Zurich in Basel. The periplasm, the space between the inner cytoplasmic membrane and the bacterial outer membrane in gram-negative bacteria, contains low concentrations of metalloenzymes inhibitors, such as glutathione.

Having found ideal in vivo conditions, the authors went a step forward and decided to optimize biot-Ru–SAV by applying principles of directed evolution, a method that mimics the process of natural selection to evolve proteins with enhanced properties or activities. “We could then develop a simple and robust screening method that allowed us to test thousands of biot-Ru–SAV mutants and identify the most active variant,” Ward explains.

Not only could the authors markedly improve the catalytic properties of biot-Ru–SAV, but they could also show that organometallic-based enzymes can be engineered and optimized for different substrates, thus producing a variety of different chemical products. “The exciting thing about this is that artificial metalloenzymes like biot-Ru–SAV can be used to produce novel high added-value chemicals,” Ward says. “It has a lot of potential to combine both chemical and biological tools to ultimately utilize cells as molecular factories.”

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In a New Method for Searching Image Databases, a Hand-drawn Sketch Is all it Takes

video via University of Basel

via University of Basel

Computer scientists at the University of Basel have developed a new method for conducting image and video database searches based on hand-drawn sketches. The user draws a sketch on a tablet or interactive paper, and the system searches for a matching image in the database. The new method is free to access for researchers.

People today are increasingly confronted with the challenge of having to find their way around vast collections of photos and videos, both in their work lives and at home. Although search engines such as Google and Bing make it easy to find documents or websites quickly and efficiently using search terms, the options for searching collections of multimedia objects are more limited.

A broadly defined similarity concept

Researchers at the Department of Mathematics and Computer Science at the University of Basel have developed a system known as ‘vitrivr’, which allows a search for images and videos by means of a sketch. The user creates a sketch of the desired object on a tablet or interactive paper, and the program delivers the images and video clips that most resemble it. For videos, the user can even specify on the sketch in which direction an object is moving in the searched sequence.

In designing the system, the researchers deliberately set a very broad similarity concept and adapted it to different types of sketch; for example, similar colors, shapes or directions of movement.

Individual searches can then be augmented by a range of other query types – search terms, examples of images and videos, or combinations of all these. An important feature of the new system is its scalability, a feature that means it can be used even with very large multimedia collections.

The vitrivr system is entirely open source and is therefore freely available to the international research community. It is already being used for a wide range of purposes, from discerning patterns of movement in sports videos for the Federal Office of Sport to searching collections of digital watermarks in a collaboration with the Basel Paper Mill.

Researchers around the world are currently working on developing the system, often with the support of well-known programs, such as the Google Summer of Code. vitrivr is also the basis for the iMotion research project, which is funded by the EU and the Swiss National Science Foundation, and is set to be used as a search engine for large-scale video collections as part of a collaboration with Red Hen Labs in the US.

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Graphene could produce frictionless coatings

A graphen nanoribbon was anchored at the tip of a atomic force microscope and dragged over a gold surface. The observed friction force was extremely low. CREDIT University of Basel, Department of Physics

A graphen nanoribbon was anchored at the tip of a atomic force microscope and dragged over a gold surface. The observed friction force was extremely low.
CREDIT
University of Basel, Department of Physics

Graphene, a modified form of carbon, offers versatile potential for use in coating machine components and in the field of electronic switches.

An international team of researchers led by physicists at the University of Basel have been studying the lubricity of this material on the nanometer scale. Since it produces almost no friction at all, it could drastically reduce energy loss in machines when used as a coating, as the researchers report in the journal Science.

In future, graphene could be used as an extremely thin coating, resulting in almost zero energy loss between mechanical parts. This is based on the exceptionally high lubricity — or so-called superlubricity — of modified carbon in the form of graphene. Applying this property to mechanical and electromechanical devices would not only improve energy efficiency but also considerably extend the service life of the equipment.

Fathoming out the causes of the lubricant behavior

An international community of physicists from the University of Basel and the Empa have studied the above-average lubricity of graphene using a two-pronged approach combining experimentation and computation. To do this, they anchored two-dimensional strips of carbon atoms — so-called graphene nanoribbons — to a sharp tip and dragged them across a gold surface. Computer-based calculations were used to investigate the interactions between the surfaces as they moved across one another. Using this approach, the research team led by Prof. Ernst Meyer at the University of Basel is hoping to fathom out the causes of superlubricity; until now, little research has been carried out in this area.

By studying the graphene ribbons, the researchers hope to learn about more than just the slip behavior. Measuring the mechanical properties of the carbon-based material also makes sense because it offers excellent potential for a whole range of applications in the field of coatings and micromechanical switches. In future, even electronic switches could be replaced by nanomechanical switches, which would use less energy for switching on and off than conventional transistors.

The experiments revealed almost perfect, frictionless movement. It is possible to move graphene ribbons with a length of 5 to 50 nanometers using extremely small forces (2 to 200 piconewtons). There is a high degree of consistency between the experimental observations and the computer simulation.

A discrepancy between the model and reality appears only at greater distances (five nanometers or more) between the measuring tip and the gold surface. This is probably because the edges of the graphene nanoribbons are saturated with hydrogen, which was not accounted for in the simulations.

“Our results help us to better understand the manipulation of chemicals at the nano level and pave the way for creating frictionless coatings,” write the researchers.

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Ideal Single-Photon Source: Quantum cryptography and quantum communication

Semiconductor quantum dot emitting a stream of identical photons.

Semiconductor quantum dot emitting a stream of identical photons.

With the help of a semiconductor quantum dot, physicists at the University of Basel have developed a new type of light source that emits single photons. For the first time, the researchers have managed to create a stream of identical photons.

They have reported their findings in the scientific journal Nature Communications together with colleagues from the University of Bochum.

A single-photon source never emits two or more photons at the same time. Single photons are important in the field of quantum information technology where, for example, they are used in quantum computers. Alongside the brightness and robustness of the light source, the indistinguishability of the photons is especially crucial. In particular, this means that all photons must be the same color. Creating such a source of identical single photons has proven very difficult in the past.

However, quantum dots made of semiconductor materials are offering new hope. A quantum dot is a collection of a few hundred thousand atoms that can form itself into a semiconductor under certain conditions. Single electrons can be captured in these quantum dots and locked into a very small area. An individual photon is emitted when an engineered quantum state collapses.

Noise in the semiconductor

A team of scientists led by Dr. Andreas Kuhlmann and Prof. Richard J. Warburton from the University of Basel have already shown in past publications that the indistinguishability of the photons is reduced by the fluctuating nuclear spin of the quantum dot atoms. For the first time ever, the scientists have managed to control the nuclear spin to such an extent that even photons sent out at very large intervals are the same color.

Quantum cryptography and quantum communication are two potential areas of application for single-photon sources. These technologies could make it possible to perform calculations that are far beyond the capabilities of today’s computers.

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