Designing materials that have the necessary properties to fulfill specific functions is a challenge faced by researchers working in areas from catalysis to solar cells. To speed up development processes, modeling approaches can be used to predict information to guide refinements. Researchers from The University of Tokyo Institute of Industrial Science have developed a machine learning model to determine characteristics of bonded and adsorbed materials based on parameters of the individual components. Their findings are published in Applied Physics Express. 

Factors such as the length and strength of bonds in materials play crucial roles in determining the structures and properties we experience on the macroscopic scale. The ability to easily predict these characteristics is therefore valuable when designing new materials.

The density of states (DOS) is a parameter that can be calculated for individual atoms, molecules, and materials. Put simply, it describes the options available to the electrons that arrange themselves in a material. A modeling approach that can take this information for selected components and produce useful data for the desired product, with no need to make and analyze the material, is an attractive tool.

The researchers used a machine learning approach where the model refines its response without human intervention to predict four different properties of products from the DOS information of the individual components. Although the DOS has been used as a descriptor to establish single parameters before, this is the first time multiple different properties have been predicted.

"We were able to quantitatively predict the binding energy, bond length, number of covalent electrons, and the Fermi energy after bonding for three different general types of system," explains study first author Eiki Suzuki. "And our predictions were very accurate across all of the properties."

Because the calculation of DOS of an isolated state is less complex than for bonded systems, the analysis is relatively efficient. In addition, the neural network model used performed well even when only 20% of the dataset was used for training.

"A significant advantage of our model is that it is general and can be applied to a wide variety of systems," study corresponding author Teruyasu Mizoguchi explains. "We believe that our findings could make a significant contribution to numerous development processes, for example in catalysis, and could be particularly useful in newer research areas such as nanoclusters and nanowires."

Climate change is driving a large increase in intense, slow-moving storms, a new study by Newcastle University and the Met Office has found.

Investigating how climate affects intense rainstorms across Europe, climate experts have shown there will be a significant future increase in the occurrence of slow-moving intense rainstorms. The scientists estimate that these slow-moving storms may be 14 times more frequent across the land by the end of the century. It is these slow-moving storms that have the potential for very high precipitation accumulations, with devastating impacts, as we saw in Germany and Belgium. 

Led by Dr. Abdullah Kahraman, of Newcastle University's School of Engineering, the researchers used very detailed climate model simulations at the UK Met Office Hadley Centre. They found that slower storm movement acts to increase the amount of rainfall that accumulates locally, increasing the risk of flash floods across Europe beyond what has been expected based on previous studies.

Published in the journal Geophysical Research Letters, the study results show that storms producing intense rain may move slower with climate change, increasing the duration of exposure to these extremes.

Dr. Abdullah Kahraman, who is also a visiting scientist at the Met Office, said: "With recent advances in supercomputer power, we now have pan-European climate simulations resolving the atmosphere in high detail as short-range weather forecasting models do. These models have grid spacing of approximately 2 km, which allows them to simulate storm systems much better, resulting in a better representation of extremes.

"Using these state-of-the-art climate simulations, we have developed metrics to extract potential cases for heavy rainfall and a smaller, almost-stationary subset of these cases with the potential for high rainfall accumulations. These metrics provide a holistic view of the problem and help us understand which factors of the atmosphere contribute to heavy rainfall changes.

"This is one of the first studies to explore changes in the speed of such heavy rainfall systems - an important aspect contributing to flooding risk. Currently, we are also investigating other extreme weather types by examining the climate simulations data with a severe weather forecaster's perspective."

Professor Hayley Fowler, of Newcastle University's School of Engineering, added: "Governments across the world have been too slow in reducing greenhouse gas emissions and global warming continues apace. This study suggests that changes to extreme storms will be significant and cause an increase in the frequency of devastating flooding across Europe. This, alongside the current floods in Europe, is the wake-up call we need to produce improved emergency warning and management systems, as well as implementing climate change safety factors into our infrastructure designs to make them more robust to these severe weather events."

Professor Lizzie Kendon, Science Fellow at the Met Office and Professor at Bristol University, said: "This study shows that in addition to the intensification of rainfall with global warming, we can also expect a big increase in slow-moving storms which have the potential for high rainfall accumulations. This is very relevant to the recent flooding seen in Germany and Belgium, which highlights the devastating impacts of slow-moving storms.

"Our finding that slow-moving intense rainstorms could be 14 times more frequent by the end of the century under the high emissions RCP8.5 scenario, shows the serious impacts that we may expect across Europe if we do not curb our emissions of greenhouse gases."

The study findings are relevant to climate mitigation and adaptation policy in Europe, with specific implications for future flooding impacts, the design of infrastructure systems, and the management of water resources.

Currently, almost stationary intense rainstorms are uncommon in Europe and happen rarely over parts of the Mediterranean Sea. Accurate predictions of future changes in intense rainfall events are key to putting effective adaptation and mitigation plans in place to limit the adverse impacts of climate change.