Symmetry is a prevalent feature of nature at all scales. For example, our naked eyes can easily identify symmetries in the bodily shape of countless organisms. Symmetry is also very important in the fields of physics and chemistry, especially in the microscopic realm of atoms and molecules. Crystals, which are highly ordered materials, can even have multiple types of symmetry at the same time, such as rotational symmetry, inversion symmetry, and translational symmetry.

Lately, alongside rapid progress in computer science, researchers have developed computational methods that seek to predict the physical properties of crystals based on their electronic structure. In practice, however, pure and perfectly symmetric crystals are seldom used. This is because a crystal’s properties can be tuned as desired by alloying them with other materials or randomly substituting certain atoms with other elements, i.e., doping.

Accordingly, materials scientists are seeking computationally efficient approaches to analyze such alloys and substituted crystals, also known as solid solutions. The ‘supercell method’ is one such approach and is widely used to model crystal structures with random substitutions of different atoms. The symmetry of crystals, however, is actually a problem when using this technique. In crystals, there can be many substitution patterns that are physically equivalent to other substitutions if we simply translate or rotate them. Findings of these symmetric substitution patterns are not very meaningful, and thus their calculation when using the supercell method is a waste of time.

In a recent study, a team of researchers led by Assistant Professor Kousuke Nakano from the Japan Advanced Institute of Science and Technology (JAIST) found a solution to this problem. They developed an open-source software called “Suite for High-throughput generation of models with atomic substitutions implemented by Python,” or SHRY that can, in terms of symmetry, generate distinct substitution patterns in solid solutions and alloys [https://github.com/giprayogo/SHRY]. This work, which was published in the ACS Journal of Chemical Information and Modeling, was co-authored by doctoral student Genki I. Prayogo, Dr. Andrea Tirelli, Professor Ryo Maezono, and Associate Professor Kenta Hongo.

The team approached the problem from the angle of group theory. It turns out that searching for atomic substitution patterns in crystals is analogous to the problem of finding coloring patterns on the vertices of graphs under certain restrictions. This allows one to reformulate the original problem of finding non-symmetric atomic substitutions in crystals as exploring search trees depicting the coloring of vertices in graphs.
However, the way in which the search tree is explored is crucial. A simple, naïve approach in which all possible branches are searched and directly compared is impossible; the time and calculations required to grow uncontrollably for large systems. This happens because deciding whether to explore further down a branch requires information about all other branches besides the one being explored, which is technically referred to as ‘non-local information’.

To avoid this issue, the researchers implemented in SHRY a technique called canonical augmentation. “This method can decide whether a tree branch should be explored more deeply or not based solely on local information,” explains Dr. Nakano, “Most importantly, theorems from group theory guarantee that only distinct substitution patterns will be extracted, without over-or under-exploring the tree structure in the terms of symmetry.” The team verified that their algorithm was error-free by testing it thoroughly with data from a database of crystal structures.

It is worth noting that SHRY was written in Python 3, one of the most popular cross-platform programming languages, and uploaded to GitHub, a leading project-sharing online platform. “SHRY can be used as a stand-alone program or imported into another Python program as a module,” highlights Dr. Nakano, “Our software also uses the widely supported Crystallographic Information File (CIF) format for both the input and output of the sets of substituted crystal structures.” The team plans to keep improving SHRY’s code based on feedback from other users, boosting its speed and capabilities.

Overall, the software developed in this study could help scientists identify potential atomic substitutions in solids, which is the most common strategy used to tune the properties of materials for practical applications. SHRY will help speed up research and develop substituted crystals with unprecedented functionalities and superior characteristics.

As rising sea levels cause marshes to move inland in six mid-Atlantic states, the coastal zone will not continue to serve as a carbon sink but release more carbon into the atmosphere, a new modeling study led by researchers at Duke University finds.

Earlier estimates focused on the potential for an expanded area of coastal marshes to capture more carbon, removing it from the atmosphere where it acts as a greenhouse gas in the form of carbon dioxide. But as coastal marshes invade low-lying forests and freshwater wetlands, the loss of trees and decomposition will release more carbon into the air than can be captured by the marshes, further contributing to global climate change.

The study was conducted in consultation with natural resource agencies in North Carolina, New York, New Jersey, Delaware, Maryland, and Virginia. Maps of predicted changes in coastal habitats and carbon due to sea-level rise were created to support coastal planning.

“This research and our conversations with the states raise lots of questions about options for managing coastal landscapes given these changes, and emphasizes the importance of reducing greenhouse gases and sea-level rise overall because that's the main driver of all of this,” said Katie Warnell, lead author of the study and a policy associate at Duke’s Nicholas Institute for Environmental Policy Solutions. “Carbon is one piece of the picture. There are many other reasons to keep marshes around, including coastal protection and nursery habitats for fisheries. We need to weigh all of these different factors in making decisions about managing our coastal habitats.”

The peer-reviewed, open-access study was published on June 23 in the journal PLOS Climate.

The modeling runs looked at land changes in coastal areas through the year 2104 in scenarios that predict intermediate sea level rise. In 16 out of the 19 runs of the model, inland marsh migration converted the land from a net carbon sink to a net carbon source.

“There might be some things that can be done to protect key areas from converting,” Warnell said. “In North Carolina, berms and pumps have been used to protect agricultural land and towns from sea level rise. While these are expensive, they might be worth it in certain areas.”

Another possible option, said Warnell, is preemptive forest harvest in vulnerable areas to prevent carbon from entering the atmosphere upon decomposition. As the sea level rises and causes saltwater to replace freshwater, trees in certain low-lying areas are dying and forming ominous-looking “ghost forests.” The tree deaths reduce carbon storage and emit carbon through decomposition.

“In this new study, Warnell and others have made initial estimates of the carbon costs associated with the drowning and salinization of coastal wetland ecosystems,” said Emily Bernhardt, a professor in Duke’s Nicholas School of the Environment who has extensively studied ghost forests in the eastern United States. “These early estimates suggest that habitat transitions caused by a sea-level rise across the Mid Atlantic coastal plain will shift coastal ecosystems from carbon sinks to carbon sources without thoughtful intervention.”

Ryan Day studies superconductors. Materials that conduct electricity perfectly, losing no energy to heat and resistance. Specifically, the University of California, Berkeley scientist studies how superconductors can coexist with their opposites; insulating materials that stop the flow of electrons. 

The materials that combine these two opposed states, called topological superconductors, are understandably weird, and hard to characterize and engineer, but if one could design them properly, they could play an important role in quantum supercomputing.

“Every computer is prone to error, and that is no different when you move to quantum computing— it just gets a lot harder to manage. Topological quantum computing is one of the platforms thought to be able to circumvent many of the most common sources of error,” says Day, “but topological quantum computing requires that we fabricate a particle which has never been seen before in nature.”

Day came to the Canadian Light Source at the University of Saskatchewan to use the QMSC beamline, a facility built to explore exactly these types of questions in quantum materials. The capabilities were developed under the leadership of Andrea Damascelli, Scientific Director of the Stewart Blusson Quantum Matter Institute at UBC, with whom Day was a doctoral student at the time of this research.

“QMSC was developed to have very fine control over a very wide range of energies, so you can really get exceptionally precise information about the electrons as they move in all possible directions,” said Day.

His experiment, performed at temperatures around 20 degrees above absolute zero, aimed to resolve conflicting results in the existing research on superconductors with topological states.

“The experiments that had been done before ours were really good, but there were some contradictions in the literature that needed to be understood better,” he explained. The relative newness of the field, combined with the unusual properties that materials display in the energy ranges used for this research, meant it was difficult to disentangle what was going on with the topological states.

In his experiments, Day observed that the topological states were embedded in a large number of other electronic states which inhibit lithium iron arsenide — the superconducting material he’s studying — from exhibiting topological superconductivity. Based on his measurements at the CLS, he has proposed that this problem can be circumvented by simply stretching the material.

The results of this work, published in Physical Review B, provide further evidence that lithium iron arsenide does support topological states on its surface, key to potentially using the material in quantum computing. It also reveals potential challenges to engineering materials for these applications, an area for future research.

“By doing these experiments, we can understand this material in a much better way and begin to think about how we can actually make use of it, and then hopefully someone builds a quantum computer with it and everyone wins.”

The last two decades have seen the development of sophisticated computational tools that explore the DNA of bacteria. These tools are on the lookout for interesting metabolites (metabolism-related molecules) that elicit a strong biological reaction. Their impact might be toxic, or it might be life-enhancing; for example, informing the development of new antibiotics, anti-cancer drugs, or bio-based insecticides for use in agriculture. 

The computational tools mine for specific genetic signatures in the neighborhood of DNA that are responsible for producing various compounds of clinical, agricultural, and industrial interest. However, how these genetic regions are linked to global parts of the bacterial systems remains a mystery.

A team of scientists from the Technical University of Denmark (DTU) and the University of California San Diego has developed a novel computational approach for analyzing the DNA sequences of thousands of bacteria. The results of their study have been published in the KeAi journal Synthetic and Systems Biotechnology.

Tilmann Weber is Associate Director of the Natural Product Genome Mining group at the Novo Nordisk Foundation Center for Biosustainability at DTU (DTU Biosustain) and is one of the study authors. According to Weber, their goal was to unravel which genomic parts work in unison to produce compounds of great interest.

He explains: “Enterobacteriaceae is a large family of bacteria that includes common infectious pathogens, such as Salmonella and E-coli, as well as harmless bacteria that live in symbiosis with other living beings. Surprisingly, the computational analysis conducted for this study identified a large number of gene clusters responsible for metabolites of potential interest that were previously unknown. We still need to figure out the functions of these compounds in the production of bacteria, as well as their function when they interact with human hosts or other environments.”

Research has shown that several gut microbes produce a molecule called colibactin that can be associated with colon cancer. In this study, the team established a variety of genetic elements that are always present in colibactin-containing bacteria. Omkar Mohite, a postdoctoral researcher at DTU Biosustain and the first author of the study, notes: “Such associated signatures could help in predicting a list of biological parts that come together to support the production of the genotoxin that can cause colon cancer – valuable information that might help to improve treatment options in the future.”

He adds: “The drive to understand how biological systems are made up of several parts interacting together is what got me into science. I believe that this puzzle can be solved with new approaches to the integration and investigation of large numbers of datasets, like the approach we’ve used in this study.”