Microsoft Looks to Speed Up Materials Science Research with Quantum-Compatible System

 Microsoft Looks to Speed Up Materials Science Research with Quantum-Compatible System

Nextgov

Alexandra Kelley, June 21, 2023

Microsoft's newly announced Azure Quantum Elements system aims to support and emulate properties of future quantum computing technologies so researchers can sift through molecules' constituent atom combinations to accelerate materials discovery. The system is designed to interoperate with a future scaled quantum computer and engineered to coordinate with a quantum computer to run accurate models for testing atom combinations. Microsoft said the system would help scientists refine which combinations yield useful molecules via artificial intelligence and machine learning algorithms trained on large datasets. Microsoft CEO Satya Nadella said, "Our goal is to compress the next 250 years of chemistry and materials science progress into the next 25."

Novel 3D Printing Method

New direction,new materials.

Novel 3D printing method a 'game changer' for discovery, manufacturing of new materials  by Karla Cruise, University of Notre Dame

The design strategy of HTCP. a, Schematic illustration of the combinatorial printing method based on in situ aerosol mixing. b, Orthogonal and parallel gradient printing design strategies, and corresponding printed gradient patterns using blue ink (food dye Blue 1) and red ink (rhodamine B), demonstrating a compositional–modulation feature. c, Optical microscopy images showing the impact of aerosol ink flow rate on the deposited materials. Scale bar, 100 µm. d, Printed material thickness versus flow rate of various inks (polystyrene, AgNW, graphene and Bi2Te3). Error bars represent s.d. from four experimental replicates. sccm, standard cubic centimeters per minute. Credit: Nature (2023). DOI: 10.1038/s41586-023-05898-9

The time-honored Edisonian trial-and-error process of discovery is slow and labor-intensive. This hampers the development of urgently needed new technologies for clean energy and environmental sustainability, as well as for electronics and biomedical devices.

"It usually takes 10 to 20 years to discover a new material," said Yanliang Zhang, associate professor of aerospace and mechanical engineering at the University of Notre Dame.

"I thought if we could shorten that time to less than a year—or even a few months—it would be a game changer for the discovery and manufacturing of new materials."

Now Zhang has done just that, creating a novel 3D printing method that produces materials in ways that conventional manufacturing can't match. The new process mixes multiple aerosolized nanomaterial inks in a single printing nozzle, varying the ink mixing ratio on the fly during the printing process. This method—called high-throughput combinatorial printing (HTCP)—controls both the printed materials' 3D architectures and local compositions and produces materials with gradient compositions and properties at microscale spatial resolution.

His research was just published in Nature.

The aerosol-based HTCP is extremely versatile and applicable to a broad range of metals, semiconductors and dielectrics, as well as polymers and biomaterials. It generates combinational materials that function as "libraries," each containing thousands of unique compositions.

Combining combinational materials printing and high-throughput characterization can significantly accelerate materials discovery, Zhang said. His team has already used this approach to identify a semiconductor material with superior thermoelectric properties, a promising discovery for energy harvesting and cooling applications.

In addition to speeding up discovery, HTCP produces functionally graded materials that gradually transition from stiff to soft. This makes them particularly useful in biomedical applications that need to bridge between soft body tissues and stiff wearable and implantable devices

In the next phase of research, Zhang and the students in his Advanced Manufacturing and Energy Lab plan to apply machine learning and artificial intelligence-guided strategies to the data-rich nature of HTCP in order to accelerate the discovery and development of a broad range of materials.  ... ' 

Background of Approaches using Deep Learning and Materials

Value of Deep Learning with Materials:  Via ChatGPT  5/14/2023,   See previous post

Deep learning is a subset of machine learning that uses artificial neural networks to model and solve complex problems. It has found numerous applications in materials science and engineering, ranging from the prediction of material properties to the design of novel materials with specific functionalities.

One of the key advantages of deep learning in materials science is its ability to learn from large datasets of materials data, allowing researchers to identify complex patterns and relationships that may be difficult or impossible to discern through traditional methods. This can lead to more accurate predictions of material properties, as well as the discovery of new materials with desirable properties.

Some examples of deep learning applications in materials science include:

Predicting the properties of materials: Deep learning can be used to predict various material properties such as melting point, conductivity, and strength, based on data from previous experiments or simulations. This can help accelerate the development of new materials for specific applications.

Designing new materials: Deep learning algorithms can be used to search for new materials with specific properties, based on desired criteria. For example, researchers have used deep learning to design new thermoelectric materials with high efficiency for energy conversion.

Analyzing images of materials: Deep learning can be used to analyze images of materials at different scales, from atomic structures to microstructures. This can help researchers better understand the properties and behavior of materials, as well as identify defects or anomalies.

Overall, deep learning has the potential to revolutionize materials science and accelerate the development of new materials with desirable properties and functionalities.  ... 

Exploring Material Interiors with Deep Learning

Previously Mentioned, now in more detail..

Machine learning & AI

Deep-learning system explores materials' interiors from the outside   by David L. Chandler , Massachusetts Institute of Technology 

Maybe you can't tell a book from its cover, but according to researchers at MIT you may now be able to do the equivalent for materials of all sorts, from an airplane part to a medical implant. Their new approach allows engineers to figure out what's going on inside simply by observing properties of the material's surface.

The team used a type of machine learning known as deep learning to compare a large set of simulated data about materials' external force fields and the corresponding internal structure, and used that to generate a system that could make reliable predictions of the interior from the surface data.

The results have been published in the journal Advanced Materials, in a paper by doctoral student Zhenze Yang and professor of civil and environmental engineering Markus Buehler.

"It's a very common problem in engineering," Buehler explains. "If you have a piece of material—maybe it's a door on a car or a piece of an airplane—and you want to know what's inside that material, you might measure the strains on the surface by taking images and computing how much deformation you have. But you can't really look inside the material. The only way you can do that is by cutting it and then looking inside and seeing if there's any kind of damage in there."

It's also possible to use X-rays and other techniques, but these tend to be expensive and require bulky equipment, he says. "So, what we have done is basically ask the question: Can we develop an AI algorithm that could look at what's going on at the surface, which we can easily see either using a microscope or taking a photo, or maybe just measuring things on the surface of the material, and then trying to figure out what's actually going on inside?" That inside information might include any damages, cracks, or stresses in the material, or details of its internal microstructure.

The same kind of questions can apply to biological tissues as well, he adds. "Is there disease in there, or some kind of growth or changes in the tissue?" The aim was to develop a system that could answer these kinds of questions in a completely noninvasive way.

Achieving that goal involved addressing complexities including the fact that "many such problems have multiple solutions," Buehler says. For example, many different internal configurations might exhibit the same surface properties. To deal with that ambiguity, "we have created methods that can give us all the possibilities, all the options, basically, that might result in this particular [surface] scenario."

The technique they developed involved training an AI model using vast amounts of data about surface measurements and the interior properties associated with them. This included not only uniform materials but also ones with different materials in combination. "Some new airplanes are made out of composites, so they have deliberate designs of having different phases," Buehler says. "And of course, in biology as well, any kind of biological material will be made out of multiple components and they have very different properties, like in bone, where you have very soft protein, and then you have very rigid mineral substances."  ... ' 

AI for Materials Discovery

New, lucrative  and very interesting space

Artificial Intelligence for Materials Discovery

By Don Monroe

Communications of the ACM, April 2023, Vol. 66 No. 4, Pages 9-11   10.1145/3583080

3D chemical compounds floating in space, illustration  ... 

The software-driven successes of deep learning have been profound, but the real world is made of materials. Researchers are turning to artificial intelligence (AI) to help find new materials to provide better electronics and transportation, and the energy to run them.

Despite its undeniable power, however, "Machine learning, especially the deep learning revolution, relies heavily on large amounts of data," said Carla Gomes, a computer scientist at Cornell University. "This is not how science works. [Scientists] don't just memorize things."

"Machine learning as we know it is not enough for scientific discovery," she said. "We still have a long way to go."

Nevertheless, researchers are off to a promising start in addressing materials science.

Combinatorial Explosion

One of the challenges in materials discovery is the astronomical number of compositions that might have interesting properties. "High-entropy alloys" (HEA), for example, combine four or more metals. "If you consider all the elements in the periodic table and you will find that you have many combinations, then infinite combinations of the different elements, so that makes prediction very difficult," explained Ziyuan Rao, a postdoc at the Max Planck Institute for Iron Research in Düsseldorf, Germany.

Nonetheless, Rao and his colleagues created a multistage analysis to search for alloys with low thermal expansion, which are important for cryogenic storage of liquified natural gas and for other purposes. The analysis draws on extensive materials datasets, but the available compositions are a tiny, sparse subset of the universe of perhaps 1050 possibilities.

After training a machine-learning model with this data, the researchers used it to select promising candidates, often completely novel. They then used computationally intensive density-functional theory (DFT) calculations to get more precise estimates of each compound's properties. DFT is a widely used shortcut around full quantum mechanical theory. In fact, researchers at DeepMind recently used deep learning to let DFT determine how electron charge is distributed between competing atoms, a longstanding challenge.

A key feature of the HEA search is active learning, which suggests new compositions to examine that will be most informative. "it's a little different from traditional machine learning," Rao said, which typically aims to increase the accuracy of the model. "We also want to use this model to predict new materials with very good properties."

Indeed, Rao and his colleagues further refined their search by experimentally making and measuring some of the best candidates. "You need real-world data," he said, because "Simulated data sometimes is inaccurate." The experimental results are folded back into the modeling, and the loop is repeated six times. The study successfully identified two new alloy compositions with a tiny thermal expansion coefficient, less than two parts per million per degree.  ... ' 

Programmable Materials Changing Shapes

Notable methods and applications

Changing shapes at the push of a button  from Fraunhofer,  Research News / January 02, 2023

Programmable materials are true shapeshifters. They can change their characteristics in a controlled and reversible way with the push of a button, independently adapting to fit new conditions. They can be used, for example, to make comfy chairs or mattresses that prevent bedsores. To produce these, the support is formed in such a way that the contact surface is large which, as a result, lowers the pressure on parts of the body. This type of programmable material is being developed by researchers at the Fraunhofer Cluster of Excellence Programmable Materials CPM, who plan to bring it to the market with the help of industry partners. One of their goals is to reduce the use of resources.

Above: Stiffness and shape change can be locally adjusted by patterning a film. Below: Stacking foils of different heights allows the creation of a programmable material.

Left: Unit cell made up of structural elements; Center: Material structure comprised of multiple cells; Right: 3D-printed demonstrator

Many people across the world are bedridden – be it due to illness, an accident or old age. Because those affected often cannot move or turn over by themselves, they often end up with very painful bedsores. In the future, it should be possible to avoid bedsores with the help of materials that can be programmed to entirely adapt their form and mechanical properties. For example, the body support of mattresses made from programmable materials can be adjusted in any given area at the push of a button. Furthermore, the support layer is formed in such a way that strong pressure on one point can be distributed across a wider area. Areas of the bed where pressure is placed are automatically made softer and more elastic. Caregivers can also adjust the ergonomic lying position to best fit their patient.   ... ' 

Novel 3D Printing Method to Fabricate Metal-Plastic Composite Structures

 Metal-Plastic composites ...

Novel 3D Printing Method to Fabricate Metal-Plastic Composite Structures

Waseda University (Japan), November 30, 2022

A new multimaterial digital light processing three-dimensional printing (MM-DLP3DP) technique developed by scientists in Japan and Singapore can manufacture complex metal-plastic composite structures. The process starts by adding palladium ions to light-cured resins to prepare active precursors for the three-dimensional (3D) printing process that promote electroless plating (ELP), in order to form a metal coating. The MM-DL3DP apparatus then fabricates microstructures containing nested areas of the resin or the active precursor. These materials are directly plated, with 3D metal patterns added through ELP. The process allows the deposition and precise control of highly specific metal patterns on the composite structures. ... ' 

Scientists Use Machine Learning to Accelerate Materials Discovery

 The considerable value of materials discovery.

Scientists Use Machine Learning to Accelerate Materials Discovery

By Argonne National Laboratory, October 6, 2022

The final product of the machine learning algorithm: metastable phase diagrams for carbon.

Credit: Argonne National Laboratory

Scientists at the U.S. Department of Energy's Argonne National Laboratory have recently demonstrated an automated process for identifying and exploring promising new materials by combining machine learning and high performance computing. The approach could help accelerate the discovery and design of useful materials.

Using the single element carbon as a prototype, the algorithm predicted the ways in which atoms order themselves under a wide range of temperatures and pressures to make up different substances. From there, it constructed a series of what scientists call phase diagrams — a kind of map that helps guide their search for new and useful states of matter. The study is published in Nature Communications.

"We trained a computer to probe, question, and learn how carbon atoms could be organized to create phases that we might not find on earth or that we don't fully understand, thereby automating a whole step in the materials development process," says Pierre Darancet, an Argonne scientist and author on the study. "The more of this process a computer can handle on its own, the more materials science we can get done."

From Argonne National Laboratory  View Full Article   

Predictive Database for Materials

Materials science becoming increasingly important

 Nanoengineers Develop Predictive Database for Materials

UC San Diego Today, Emerson Dameron, November 28, 2022

The M3GNet algorithm developed by nanoengineers at the University of California, San Diego (UCSD)'s Jacobs School of Engineering can forecast the structure and dynamic properties of any material almost instantaneously. Researchers used M3GNet to compile the matterverse.ai database of more than than 31 million yet-to-be-synthesized materials with traits predicted by machine learning algorithms. UCSD's Shyue Ping Ong and colleagues combined graph neural networks with many-body interactions into a highly accurate deep learning framework that operates across the entire periodic table. The team employed the Materials Project's database of materials energies, forces, and stresses to train the predictive M3GNet interatomic potential model. "We truly believe that the M3GNet architecture is a transformative tool that can greatly expand our ability to explore new material chemistries and structures," said Ong.

Full Article    

Technology for Textiles

 Lately have had several questions regarding related tech.  Here an overview

Technology Transforms Textiles  By Samuel Greengard,  Commissioned by CACM Staff, November 17, 2022

It is easy to overlook the prominent role of fabrics and textiles in our lives. We wear them, we sleep on them, and we use them to cover windows and floors. Although we've witnessed a steady stream of advances in materials, fabrics haven't changed much over the years.

That is about to change. New types of fabrics are rapidly taking shape. Unlike past smart textiles that were mostly used to track motion or for visual displays, these robotic fabrics sense motion and movements and adapt accordingly. This makes them ideal for use in athletic training, rehabilitation, and prosthetics.

"Soft artificial muscle filaments … can be programmed to generate desired motion, deformation, and force," explains Thanh Nho Do, a senior lecturer in the Graduate School of Biomedical Engineering and Tyree Foundation Institute of Health Engineering (IHealthE) at the University of New South Wales in Australia. "The possibilities in the

"Fiber-like artificial muscles could form the basis of soft robotic exoskeletons," notes  Rebecca Kramer-Bottiglio, John J. Lee Associate Professor of Mechanical Engineering and Materials Science at Yale University. The resulting textiles could lead to adaptive clothing, but also new types of household products and even lightweight, stowable, shape-changing machines, she says.

Living in a Material World

Over the last few decades, technology has become increasingly interwoven with fabrics. Synthetic fibers such as Kevlar and Spandex have changed what we wear—and improved comfort. At the same time, smart casual wear, sportswear, shoes, and even smart business suits have appeared.

Robotic textiles and fabrics reshape the concept. "Advanced fibers and fabrics have the potential to create a whole new platform for experiences through wearables," explains Ozgun Kilic Afsar, a design engineer and graduate research assistant in the Massachusetts Institute of Technology's Media Lab. "These new digital fibers can see, hear, and sense their surroundings; communicate; store and convert energy; monitor unconscious and conscious biological processes like breathing, and even form fibers and fabrics that change their shapes."

Afsar is part of a group that has produced reconfigurable OmniFiber technology using fluidic fiber actuators. The material supports artificial muscle-based textiles that can deliver haptic feedback for breathing. This could benefit athletes as they train as well as individuals recovering from surgery, for example.

MIT researchers are also developing fabrics that incorporate fiber supercapacitors, fiber diodes, fiber transducers, fiber ICs, and memory devices to sense, memorize, learn, infer situational context, and respond to the environment or body with which they're interacting. One design, 3DKnITS, relies on a special type of plastic yarn in a floor mat or garment to sense body motion with 99% accuracy.

At the University of New South Wales, Do and a team of researchers have designed fabrics with tiny silicon tubes, approximately the circumference of a piece of yarn, that are braided and woven into a fabric. Hydraulic pressure stimulation causes the fabric to take on various preprogrammed shapes and forms.

Researchers at the university are also experimenting with more futuristic concepts, such as textile sheets that could grow along human skin and create a smart wearable suit that is always a perfect fit. Do says that such a garment, which he likens to an "ironman suit," could incorporate artificial muscles that could aid rescue workers and create shape-shifting bio-mimicking robots.

"These smart textiles have great potential for many applications ranging from basic fashion to compression garments, wearable haptics, wearable assistive devices for rehabilitation and human augmentation, and soft assistive robots," Do explains.   ... '   (considerably more) 

Algorithm for 2D-to-3D Engineering Integrates Art, Nature, Science

 Interesting, How does this integrate these? 

Algorithm for 2D-to-3D Engineering Integrates Art, Nature, Science

Penn Engineering Today

Devorah Fischler, October 31, 2022

Researchers at the University of Pennsylvania School of Engineering and Applied Science (Penn Engineering) and the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory have developed what they are calling a universal algorithm that permits two-dimensional materials to retain their lightness and durability when converted into three-dimensional (3D) structures. The algorithm allows hard materials to keep their mechanical strength after cutting by mimicking the structure of nacre, mollusks' natural shell coating. The algorithm can generate a computational map of cuts that are optimized for stacking, ensuring they never overlap with one another to compensate for necessary defects; fortifying tabs further bolster mechanical strength.

Full Article  

System Designs Heat-Conducting Nanomaterials

Conducting heat in materials.

System Designs Heat-Conducting Nanomaterials

MIT News

Adam Zewe, October 7, 2022

Massachusetts Institute of Technology (MIT) researchers have created an algorithm and software for designing a specific type of heat-conducting nanoscale material that could be used in computer chips that disperse heat on their own. Researchers adapted computational methods for designing large structures to generate nanomaterials with defined thermal characteristics. "Imagine that we transform a material into a picture, and then we find the best pixel distribution that gives us the prescribed property," said MIT's Giuseppe Romano. Researchers have produced materials that can conduct heat along a preferred direction and that can efficiently transform heat into electricity. ... ' 

Tricking Termites to Generate New Materials

 A new kind of biomimicry

Mimicking Termites to Generate Materials

California Institute of Technology

Ben Peltz,  August 26, 2022

California Institute of Technology (Caltech) scientists have developed a framework for the design of new materials that was inspired by termite nest-building. Caltech's Chiara Daraio said the researchers approached the challenge by considering limited resources, an architectural approach based on local rules. "We created a numerical program for materials' design with similar rules that define how two different material blocks can adhere to one another," she explained. The virtual growth program models the natural growth of biological structures, connecting L-shaped, I-shaped, T-shaped, and +-shaped virtual blocks whose availability is assigned a limit, mimicking the limited resources termites might encounter. The algorithm constructs an architecture on a grid, which can be rendered as two-dimensional or three-dimensional models. ... 

Progammable Materials sense Their Location

Quite interesting

Programmable Materials Can Sense Their Own Movements and Interactions

MIT News, Adam Zewe, August 10, 2022

Massachusetts Institute of Technology (MIT) scientists have created three-dimensionally (3D)-printed materials with programmable mechanical properties that can sense their movements and interaction with the environment. The researchers incorporated networks of air-filled channels into 3D-printed lattices; measuring pressure changes within these channels when the structure is squeezed, bent, or stretched gives engineers feedback on how the material is moving. Said MIT's Lillian Chin, "The idea with this work is that we can take any material that can be 3D-printed and have a simple way to route channels throughout it so we can get sensorization with structure." The researchers 3D-printed a handed shearing auxetics robot, ran it through a sequence of movements, and trained a neural network on the sensor data to accurately predict its motion. 

Quantum Algorithm Simulates Evolving State of Quantum Particles

 Scratching to understand this beyond the broadest concept.  Materials I can see.  

Quantum Algorithm Simulates Evolving State of Quantum Particles

By City College of New York

July 28, 2022, Comments

A quantum algorithm that simulates the evolving state of many interacting quantum particles over time has been developed by a multi-institutional team of scientists headed by the City College of New York's Pouyan Ghaemi.

"Our quantum-computing algorithm opens a new avenue to study the properties of materials resulting from strong electron-electron interactions," said Ghaemi. "As a result, it can potentially guide the search for useful materials, such as high-temperature superconductors." Ghaemi also believes the research could enable researchers to consider using quantum computers to explore phenomena that result from the interaction of electrons in solids.

From City College of New York

View Full Article    

3D Printing Method to Make Robotic Materials

 Notable use of piezoelectric effect,  detecting obstacles.    See full article below.  

Engineers Create Single-Step 3D Printing Method to Make Robotic Materials

By UCLA Samueli School of Engineering, June 17, 2022

University of California, Los Angeles (UCLA) engineers and colleagues designed a one-step three-dimensional (3D) printing process for manufacturing robots.

Critical to the all-in-one approach is the design and printing of piezoelectric metamaterials, which can change shape and move in response to an electric field, or generate electricity in response to physical forces.

The researchers developed the metamaterials to bend, flex, twist, rotate, expand, or contract rapidly.  They constitute an internal network of sensory, moving, and structural components that can move in response to programmed commands.

UCLA’s Huachen Cui said the two-way piezoelectric effect permits the robots to “detect obstacles via echoes and ultrasound emissions, as well as respond to external stimuli through a feedback control loop that determines how the robots move, how fast they move, and toward which target they move.”

From UCLA Samueli School of Engineering

View Full Article   

SmartMaterials Microscope

 FROM ACM TECHNEWS

Self-Driving Microscopes Discover Shortcuts to New Materials  

Scientists at the U.S. Department of Energy's Oak Ridge National Laboratory are training microscopes to find new materials faster using an intuitive algorithm. ...

Oak Ridge National Laboratory  ... 

Ancient Art Meets AI

 Reported on this before,   unexpected connection.

Ancient Art Meets AI for Better Materials Design

Argonne National Laboratory, John Spizzirri, April 7, 2022

University of Southern California (USC) researchers combined kirigami, the ancient Japanese art of paper cutting, with autonomous reinforcement learning to help improve materials design. In an effort to create a two-dimensional molybdenum disulfide structure embedded with electronics that can stretch while remaining stable, the researchers determined that a series of precise cuts could enable the thin material to stretch up to 40%. To determine the correct combination of cuts, the researchers performed simulations on the Theta supercomputer at the U.S. Department of Energy's Argonne National Laboratory. The model was trained on 98,500 simulations of kirigami design strategies involving one to six cuts; even without additional training data, it determined in a matter of seconds that 10 cuts would provide more than 40% stretchability. USC's Pankaj Rajak said, "It learned something the way a human learns, and used its knowledge to do something different."

Kirigami and AI For Materials Design

Most interesting, had seen this reported on before, worked with Argonne in the big enterprise :

Ancient art of kirigami meets AI for better materials design  in TechXplore

by John Spizzirri, Argonne National Laboratory

Kirigami is the Japanese art of paper cutting. Likely derived from the Chinese art of jiǎnzhǐ, it emerged around the 7th century in Japan, where it was used to decorate temples. Still in practice today, the kirigami artist uses one piece of paper to cut decorative designs, like birds and fish or the more intricate and popular snowflake.

But, this ancient art, which relies on exacting cuts to determine or replicate patterns, is finding more modern and practical applications in electronics. Specifically, in the manufacture of 2D stretchable materials that can play host to wearable electronics, like electronic skins for health monitoring.

The process combines the art of kirigami with an artificial intelligence technique called autonomous reinforcement learning. And to better synchronize the old with the new, researchers from the University of Southern California use the computing power available to them at the U.S. Department of Energy's (DOE) Argonne National Laboratory.

Reinforcement learning relates to learning actions that impart a reward or specific outcome. For example, through a combination of observation, repetition and innate ability, a baby giraffe learns to stand, walk and even run on the day it is born. This helps it find food and avoid danger very quickly.

"This is complex planning, it's learning," says Pankaj Rajak, a lead member of this project and a former postdoc at the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility. "The question is, can we use a similar behavior in materials design, like in this kirigami, where your objective is to create a more structured material that is highly stretchable, one cut at a time. It's a smart strategy for figuring out where the cuts should go."

The researchers set out to create a 2D molybdenum disulfide structure embedded with electronics, like a semiconductor device, that can stretch but remain stable.

Experimental scientists found that a deliberate series of exacting cuts would allow the atomically thin material to stretch considerably, upwards of 40%. But, there were a lot of possible combinations of cuts. So, what information did the AI program need to know to get the right combinations?

To provide the program with some starting data—like the environmental observations of a giraffe—Rajak conducted 98,500 simulations that consisted of a range of one to six cuts with different lengths that determined stretchability.   ....'

Surface Finishing by Laser

A laser process for surface finishing

Lotus effects by laser, Research News / February 01, 2022 By Fraunhofer Institute

Nano- and microstructures can now be incorporated into surfaces in an instant using lasers. The technology is being developed and marketed by the Dresden-based start-up Fusion Bionic, a spin-off from the Fraunhofer Institute for Material and Beam Technology IWS. The possibilities are virtually endless when it comes to laser structuring. It has the advantage of being fast and much more versatile than coatings.

Modern light interference technologies from Dresden now enable lotus effects and other refined structural tricks from the natural world to be transferred quickly to technical surfaces such as battery components, implants and even airplanes.

Modern light interference technologies from Dresden now enable lotus effects and other refined structural tricks from the natural world to be transferred quickly to technical surfaces such as battery components, implants and even airplanes.

Product surfaces can be enhanced with all kinds of different effects. The lotus effect, for example, uses a microstructure to allow any dirt that might stick to the surface to simply wash away the next time it rains. The fine ripples of shark skin, meanwhile, improve the dynamics of air and water on the outside of airplanes and ships, thus saving fuel. With nature as their inspiration, many such effects have been developed by coating or applying a film to the surface into which the microstructures are incorporated. Coatings and films can wear away, however, causing the desired effect to diminish over time. In recent years, researchers at Fraunhofer IWS and Technische Universität Dresden have developed an alternative, market-ready method of permanently applying nano- and microstructures to surfaces: Direct Laser Interference Patterning (DLIP). This process incorporates the nano- or microstructure directly into the surface using a laser in order to create biomimetic effects. It is a remarkably quick process, and can currently handle up to one square meter of surface per minute. The new technology is so promising that it led to Fusion Bionic being founded this year as a spin-off from Fraunhofer IWS. Fusion Bionic develops and markets DLIP system solutions for biomimetic surface finishing, but also provides surface functionalization services to its customers.

Fast enough for large surface areas

“For a long time, lasers were much too slow to be used for finishing surfaces with large areas compared to coating or applying films,” says Managing Director of Fusion Bionic, Dr. Tim Kunze, who founded the company together with three partners. “But with the DLIP process we’ve made the leap to processing large surface areas quickly.” Conventionally, people think of a laser as a single fine beam. Using it like a needle to make a pattern in a surface would be extremely time-consuming. The way the DLIP process works is different. First of all, it splits a single laser beam into multiple clusters of beams. To apply a pattern to the surface, these multiple laser beams are superimposed in a controlled way to create what is known as an interference pattern. This pattern can be distributed over a wider area, allowing surfaces with large areas to be processed rapidly.   .... '