Computers Math

Microscopic materials analysis is essential to achieve desirable performance in next-generation nanoelectronic devices, such as low power consumption and high speeds. However, the magnetic materials involved in such devices often exhibit incredibly complex interactions between nanostructures and magnetic domains. This, in turn, makes functional design challenging.
Traditionally, researchers have performed a visual analysis of the microscopic image data. However, this often makes the interpretation of such data qualitative and highly subjective. What is lacking is a causal analysis of the mechanisms underlying the complex interactions in nanoscale magnetic materials.
In a recent breakthrough published in Scientific Reports, a team of researchers led by Prof. Masato Kotsugi from Tokyo University of Science, Japan succeeded in automating the interpretation of the microscopic image data. This was achieved using an “extended Landau free energy model” that the team developed using a combination of topology, data science, and free energy. The model could illustrate the physical mechanism as well as the critical location of the magnetic effect, and proposed an optimal structure for a nano device. The model used physics-based features to draw energy landscapes in the information space, which could be applied to understand the complex interactions at the nanoscales in a wide variety of materials.
“Conventional analysis are based on a visual inspection of microscope images, and the relationships with the material function are expressed only qualitatively, which is a major bottleneck for material design. Our extended Landau free energy model enables us to identify the physical origin and location of the complex phenomena within these materials. This approach overcomes the explainability problem faced by deep learning, which, in a way, amounts to reinventing new physical laws,” Prof. Kotsugi explains. This work was supported by KAKENHI, JSPS, and the MEXT-Program for Creation of Innovative Core Technology for Power Electronics Grant.
When designing the model, the team made use of the state-of-art technique in the fields of topology and data science to extend the Landau free energy model. This led to a model that enabled a causal analysis of the magnetization reversal in nanomagnets. The team then carried out an automated identification of the physical origin and visualization of the original magnetic domain images.
Their results indicated that the demagnetization energy near a defect gives rise to a magnetic effect, which is responsible for the “pinning phenomenon.” Further, the team could visualize the spatial concentration of energy barriers, a feat that had not been achieved until now. Finally, the team proposed a topologically inverse design of recording devices and nanostructures with low power consumption.
The model proposed in this study is expected to contribute to a wide range of applications in the development of spintronic devices, quantum information technology, and Web 3.
“Our proposed model opens up new possibilities for optimization of magnetic properties for material engineering. The extended method will finally allow us to clarify ‘why’ and ‘where’ the function of a material is expressed. The analysis of material functions, which used to rely on visual inspection, can now be quantified to make precise functional design possible,” concludes an optimistic Prof. Kotsugi.
Story Source:
Materials provided by Tokyo University of Science. Note: Content may be edited for style and length. More

A team of engineers at UC Santa Cruz has developed a new method for remote automation of the growth of cerebral organoids — miniature, three-dimensional models of brain tissue grown from stem cells. Cerebral organoids allow researchers to study and engineer key functions of the human brain with a level of accuracy not possible with other models. This has implications for understanding brain development and the effects of pharmaceutical drugs for treating cancer or other diseases.
In a new study published in the journal Nature Scientific Reports, researchers from the UCSC Braingeneers group detail their automated, internet-connected microfluidics system, called “Autoculture.” The system precisely delivers feeding liquid to individual cerebral organoids in order to optimize their growth without the need for human interference with the tissue culture.
Cerebral organoids require a high level of expertise and consistency to maintain the precise conditions for cell growth over weeks or months. Using an automated system, as demonstrated in this study, can eliminate disturbance to cell culture growth caused by human interference or error, provide more robust results, and allow more scientists access to opportunities to conduct research with human brain models.
Autoculture also addresses variation that arises in organoid growth due to “batch effect” issues, where organoids grown at different times or at different labs under similar conditions may vary just because of the complexity of their growth. Using this uniform, automated system can reduce variation and allow researchers to better compare and validate their results.
“One of the big challenges is that these cultures are not very reproducible, and in part it’s not surprising because these are months-long experiments. You have to change media every couple of days and try to treat these cultures uniformly, which is extremely challenging,” said Sofie Salama, an acting professor of molecular, cellular and developmental biology at UCSC and an author on the study.
Unique design
Autoculture uses a microfluidic chip designed by the researchers, spearheaded by Associate Professor of Electrical and Computer Engineering Mircea Teodorescu and Biomolecular Engineering Ph.D. student Spencer Seiler. Their novel chips, created from a unique bi-layer mold, have tiny wells and channels for delivering minute amounts of liquid to the organoid, which allow the scientists to have a high level of control over nutrient concentrations and byproducts. Overall, the system uses mostly off-the-shelf, low-cost components, making it accessible and modular. More

Combining machine learning with multimodal electrochemical sensing can significantly improve the analytical performance of biosensors, according to new findings from a Penn State research team. These improvements may benefit noninvasive health monitoring, such as testing that involves saliva or sweat. The findings were published this month in Analytica Chimica Acta.
The researchers developed a novel analytical platform that enabled them to selectively measure multiple biomolecules using a single sensor, saving space and reducing complexity as compared to the usual route of using multi-sensor systems. In particular, they showed that their sensor can simultaneously detect small quantities of uric acid and tyrosine — two important biomarkers associated with kidney and cardiovascular diseases, diabetes, metabolic disorders, and neuropsychiatric and eating disorders — in sweat and saliva, making the developed method suitable for personalized health monitoring and intervention.
Many biomarkers have similar molecular structures or overlapping electrochemical signatures, making it difficult to detect them simultaneously. Leveraging machine learning for measuring multiple biomarkers can improve the accuracy and reliability of diagnostics and as a result improve patient outcomes, according to the researchers. Further, sensing using the same device saves resources and biological sample volumes needed for tests, which is critical with clinical samples with scarce amounts.
“We developed a new approach to improve the performance of electrochemical biosensors by combining machine learning with multimodal measurement,” said Aida Ebrahimi, Thomas and Sheila Roell Early Career Assistant Professor of Electrical Engineering and assistant professor of biomedical engineering. “Using our optimized machine learning architecture, we could detect biomolecules in amounts 100 times lower than what conventional sensing methods can do.”
The researchers’ methodology features a hardware/software system that enables them to automatically gather and process information based on a machine learning model that is trained to identify biomolecules in biological fluids such as saliva and sweat, which are common choices for noninvasive health monitoring.
“The machine learning-powered electrochemical diagnostic approach presented in this paper may find broader application in multiplexed biochemical sensing,” said Vinay Kammarchedu, 2022-23 Milton and Albertha Langdon Memorial Graduate Fellow in Electrical Engineering at Penn State and first author on the paper. “For example, this method can be extended to a variety of other molecules, including food and water toxins, drugs and neurochemicals that are challenging to detect simultaneously using conventional electrochemical methods.”
In their ongoing work, the researchers are applying this approach on such neurochemicals, which are difficult to detect due to similarities in their molecular structure and overlapping electrochemical signatures. More

Researchers at The University of Texas at Dallas and Novartis Pharmaceuticals Corp. have devised a computer-based platform for drug discovery that could make the process more effective, more efficient and less costly.
Dr. Baris Coskunuzer, professor of mathematical sciences at UT Dallas, and his colleagues developed an approach based on topological data analysis to screen thousands of possible drug candidates virtually and narrow the compound candidates considerably to those that are most fit for laboratory and clinical testing.
The researchers will present their findings at the 36th Conference on Neural Information Processing Systems, which will be held Nov. 28 through Dec. 9 in New Orleans.
Typically, the early phases of drug discovery involve researchers identifying a biological target, such as a protein associated with a disease of interest. The next step is to screen libraries of thousands of potential chemical compounds that might be effective or could be modified to affect the target to alleviate the disease’s cause or symptoms. The most promising candidates move on to the lengthy and expensive process of laboratory and clinical testing and regulatory approval.
“The drug-discovery process can take 10 to 15 years and cost a billion dollars,” Coskunuzer said. “Drug companies want a more cost-effective way to do this. They want to find the most promising compounds at the beginning of the process so they’re not wasting time testing dead ends.
“We have provided a completely new method of virtual screening that is computationally efficient and ranks compounds based on how likely they are to work.”
While virtual screening of libraries of chemical compounds is not new, Coskunuzer said his group’s approach significantly outperforms other state-of-the-art methods on large data sets. More

Cornell University researchers have created an interface that allows users to handwrite and sketch within computer code — a challenge to conventional coding, which typically relies on typing.
The pen-based interface, called Notate, lets users of computational, digital notebooks open drawing canvases and handwrite diagrams within lines of traditional, digitized computer code.
Powered by a deep learning model, the interface bridges handwritten and textual programming contexts: notation in the handwritten diagram can reference textual code and vice versa. For instance, Notate recognizes handwritten programming symbols, like “n,” and then links them up to their typewritten equivalents.
“A system like this would be great for data science, specifically with sketching plots and charts that then inter-operate with textual code,” said Ian Arawjo, lead author of the paper and doctoral student in the field of information science. “Our work shows that the current infrastructure of programming is actually holding us back. People are ready for this type of feature, but developers of interfaces for typing code need to take note of this and support images and graphical interfaces inside code.”
Arawjo also said the work demonstrates a new path forward by introducing artificial intelligence-powered, pen-based coding at a time when drawing tablets are becoming more widely used.
“Tools like Notate are important because they open us up to new ways to think about what programming is, and how different tools and representational practices can change that perspective,” said Tapan Parikh, associate professor of information science and paper co-author.
Story Source:
Materials provided by Cornell University. Original written by Louis DiPietro. Note: Content may be edited for style and length. More

From pangolin scales that can stand up to hard hits to colorful but sturdy peacock feathers, nature can do a lot with a few simple molecules.
In a new review paper, a team of international researchers have laid out how engineers are taking inspiration from the biological world — and designing new kinds of materials that are potentially tougher, more versatile and more sustainable than what humans can make on their own.
“Even today, nature makes things way simpler and way smarter than what we can do synthetically in the lab,” said Dhriti Nepal, first author and a research materials engineer at the Air Force Research Laboratory in Ohio.
Nepal along with Vladimir Tsukruk from Georgia Institute of Technology and Hendrik Heinz of the University of Colorado Boulder served as co-corresponding authors for the new analysis. The team published its findings Nov. 28 in the journal Nature Materials.
The researchers, who come from three countries, delve into the promise and challenges behind “bioinspired nanocomposites.” These materials mix together different kinds of proteins and other molecules at incredibly small scales to achieve properties that may not be possible with traditional metals or plastics. Researchers often design them using advanced computer simulations or models. Examples include thin films that resist wear and tear by incorporating proteins from silkworm cocoons; new kinds of laminates made from polymers and clay materials; carbon fibers produced using bioinspired principles; and panes of glass that don’t easily crack because they include nacre — the iridescent lining inside many mollusk shells.
Such nature-inspired materials could, one day, lead to new and better solar panels, soft robots and even coatings for hypersonic jets, said Heinz, professor of chemical and biological engineering at CU Boulder. But first, researchers will need to learn how to build them from the bottom up, ensuring that every molecule is in the right place. More

Researchers affiliated with the Q-NEXT quantum research center show how to create quantum-entangled networks of atomic clocks and accelerometers — and they demonstrate the setup’s superior, high-precision performance.
What happened
For the first time, scientists have entangled atoms for use as networked quantum sensors, specifically, atomic clocks and accelerometers.
The research team’s experimental setup yielded ultraprecise measurements of time and acceleration. Compared to a similar setup that does not draw on quantum entanglement, their time measurements were 3.5 times more precise, and acceleration measurements exhibited 1.2 times greater precision.
The result, published in Nature, is supported by Q-NEXT, a U.S. Department of Energy (DOE) National Quantum Information Science Research Center led by DOE’s Argonne National Laboratory. The research was conducted by scientists at Stanford University, Cornell University and DOE’s Brookhaven National Laboratory.
“The impact of using entanglement in this configuration was that it produced better sensor network performance than would have been available if quantum entanglement were not used as a resource,” said Mark Kasevich, lead author of the paper, a member of Q-NEXT, the William R. Kenan, Jr. professor in the Stanford School of Humanities and Sciences and professor of physics and of applied physics. “For atomic clocks and accelerometers, ours is a pioneering demonstration.”
What is quantum entanglement? How does it apply to sensors? Entanglement, a special property of nature at the quantum level, is a correlation between two or more objects. When two atoms are entangled, one can measure the properties of both atoms by observing only one. This is true no matter how much distance — even if it’s light-years — separates the entangled atoms. A helpful everyday analogy: A red marble and a blue marble are placed in a box. If you draw a red marble from the box, you know, without having to look at the other one, that it’s blue. The color of the marbles is correlated, or entangled. In the quantum realm, entanglement is subtler. An atom can take on multiple states (colors) at once. If our marbles were like atoms, each marble would be both red and blue at the same time. Neither is fully red or blue while it sits the box. The quantum marble “decides” its color only at the moment of revelation. And once you draw one marble of “decided” color, you know the color of its entangled partner. To take a measurement of one member of an entangled pair is effectively to take a simultaneous reading of both. Taking this further: Two entangled clocks are practically equivalent to a single clock with two displays. Time measurements taken using entangled clocks can be more precise than measurements from two separate, synchronized clocks. More

Nanoengineers at the University of California San Diego’s Jacobs School of Engineering have developed an AI algorithm that predicts the structure and dynamic properties of any material — whether existing or new — almost instantaneously. Known as M3GNet, the algorithm was used to develop matterverse.ai, a database of more than 31 million yet-to-be-synthesized materials with properties predicted by machine learning algorithms. Matterverse.ai facilitates the discovery of new technological materials with exceptional properties.
The team behind M3GNet, led by UC San Diego nanoengineering professor Shyue Ping Ong, uses matterverse.ai and the new capabilities of M3GNet in their search for safer and more energy-dense electrodes and electrolytes for rechargeable lithium-ion batteries. The project is explored in the Nov. 28 issue of the journal Nature Computational Science.
The properties of a material are determined by the arrangement of its atoms. However, existing approaches to obtain that arrangement are either prohibitively expensive or ineffective for many elements.
“Similar to proteins, we need to know the structure of a material to predict its properties.” said Ong, the associate director of the Sustainable Power and Energy Center at the Jacobs School of Engineering. “What we need is an AlphaFold for materials.”
AlphaFold is an AI algorithm developed by Google DeepMind to predict protein structure. To build the equivalent for materials, Ong and his team combined graph neural networks with many-body interactions to build a deep learning architecture that works universally, with high accuracy, across all the elements of the periodic table.
“Mathematical graphs are really natural representations of a collection of atoms,” said Chi Chen, a former senior project scientist in Ong’s lab and first author of the work, who is now a senior quantum architect at Microsoft Quantum. “Using graphs, we can represent the full complexity of materials without being subject to the combinatorial explosion of terms in traditional formalisms.”
To train their model, the team used the huge database of materials energies, forces and stresses collected in the Materials Project over the past decade. The result is the M3GNet interatomic potential (IAP), which can predict the energies and forces in any collection of atoms. Matterverse.ai was generated through combinatorial elemental substitutions on more than 5,000 structural prototypes in the Inorganic Crystal Structure Database (ICSD). The M3GNet IAP was then used to obtain the equilibrium crystal structure — a process called “relaxation” — for property prediction.
Of the 31 million materials in matterverse.ai today, more than a million are predicted to be potentially stable. Ong and his team intend to greatly expand not just the number of materials, but also the number of ML-predicted properties, including high-value properties with small data sizes using a multi-fidelity approach they developed earlier.
Beyond structural relaxations, the M3GNet IAP also has broad applications in dynamic simulations of materials and property predictions as well.
“For instance, we are often interested in how fast lithium ions diffuse in a lithium-ion battery electrode or electrolyte. The faster the diffusion, the more quickly you can charge or discharge a battery,” Ong said. “We have shown that the M3GNet IAP can be used to predict the lithium conductivity of a material with good accuracy. We truly believe that the M3GNet architecture is a transformative tool that can greatly expand our ability to explore new material chemistries and structures.”
To promote the use of M3GNet, the team has released the framework as an open-source Python code on Github. Since posting the preprint on Arxiv in Feb 2022, the team has received interest from academic researchers and those in the industry. There are plans to integrate the M3GNet IAP as a tool in commercial materials simulation packages.
This work was authored by Chi Chen and Shyue Ping Ong at UC San Diego. The research was primarily funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under the Materials Project program. Part of the work was funded by LG Energy Solution through the Frontier Research Laboratory Program. This work used the Extreme Science and Engineering Discovery Environment (XSEDE).
Story Source:
Materials provided by University of California – San Diego. Original written by Emerson Dameron. Note: Content may be edited for style and length. More