Materials Science
Assembly Line
Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage
The development of heat-resistant dielectric polymers that withstand intense electric fields at high temperatures is critical for electrification. Balancing thermal stability and electrical insulation, however, is exceptionally challenging as these properties are often inversely correlated. A traditional intuition-driven polymer design approach results in a slow discovery loop that limits breakthroughs. Here we present a machine learning-driven strategy to rapidly identify high-performance, heat-resistant polymers. A trustworthy feed-forward neural network is trained to predict key proxy parameters and down select polymer candidates from a library of nearly 50,000 polysulfates. The highly efficient and modular sulfur fluoride exchange click chemistry enables successful synthesis and validation of selected candidates. A polysulfate featuring a 9,9-di(naphthalene)-fluorene repeat unit exhibits excellent thermal resilience and achieves ultrahigh discharged energy density with over 90% efficiency at 200 °C. Its exceptional cycling stability underscores its promise for applications in demanding electrified environments.
A universal strategy for decoupling stiffness and extensibility of polymer networks
Since the invention of polymer networks such as cross-linked natural rubber in the 19th century, it has been a dogma that stiffer networks are less stretchable. We report a universal strategy for decoupling the stiffness and extensibility of single-network elastomers. Instead of using linear polymers as network strands, we use foldable bottlebrush polymers, which feature a collapsed backbone grafted with many linear side chains. Upon elongation, the collapsed backbone unfolds to release stored length, enabling remarkable extensibility. By contrast, the network elastic modulus is inversely proportional to network strand mass and is determined by the side chains. We validate this concept by creating single-network elastomers with nearly constant Young’s modulus (30 kilopascals) while increasing tensile breaking strain by 40-fold, from 20 to 800%. We show that this strategy applies to networks of different polymer species and topologies. Our discovery opens an avenue for developing polymeric materials with extraordinary mechanical properties.
A New Age of Materials Is Dawning, for Everything From Smartphones to Missiles
Modern composites, starting with Bakelite, were pioneered in the early part of the 20th century. Other composites were invented at a steady pace, and the industry began to hit its stride in the late 1990s and early 2000s, when automated processes for turning things like carbon fiber into giant structures like airplane bodies and windmill blades reached maturity.
In just the past couple of years, a number of startups have developed processes for creating all sorts of small objects from composites, in a way that is fast and inexpensive. These include Berkeley, Calif.-based Arris Composites, 9T Labs in Zurich, Orbital Composites in Silicon Valley, and others.
Arris shapes carbon fibers using a process that resembles wire bending—imagine how something like a coat hanger is made—says CEO Riley Reese. Then, those shaped fibers are put into a resin, and the resulting form is put into a custom mold that applies heat and pressure to further compress, shape and strengthen the part. 9T Labs uses a similar process, but starts by using “additive manufacturing” (similar to 3-D printing) to lay down narrow strips of carbon fiber into a particular shape, and then molding it in a way similar to Arris’s process, says Eichenhofer.
Orbital Composites is using substantially different processes, says CEO Amolak Badesha. Using off-the-shelf industrial robots with custom print heads that spit out carbon fiber, the company 3-D prints shapes in a process that resembles Harold’s purple crayon, for those familiar with the children’s book. The difference is that while Harold could draw in three dimensions any shape he liked, Orbital uses removable molds to support its carbon-fiber shapes as they’re being printed.
Altrove closes €3.7 million to develop sustainable alternatives to critical materials
Altrove, a deep tech startup creating sustainable alternatives for critical materials, has raised €3.7 million in a pre-seed funding round led by Contrarian Ventures, Emblem and strong angel investors like Thomas Clozel (CEO Owkin), Julien Chaumond (CTO HuggingFace) and Nikolaj Deichmann (3Shape). The funding will accelerate the development of their core technology, currently focusing on substitutes for rare-earth compounds used in transition technologies, electric vehicles, and advanced electronics. The company was founded in early 2024 by Cambridge alumnus Dr. Joonatan Laulainen and second-time founder Thibaud Martin at Entrepreneur First.
Unlike recent AI materials startups, Altrove does not stop at predicting what new materials could exist but focuses on finding the optimal recipe to manufacture alternative materials at scale. Altrove developed a proprietary characterization technology to iteratively learn from each experiment, accelerating the discovery process up to 100 times.
Pioneering the future of materials extraction
The company’s breakthrough lies in a new silicon membrane technology that can be adjusted to efficiently recover disparate materials, providing a more sustainable and economically viable alternative to conventional, chemically intensive processes. Think of a colander with adjustable pores to strain different types of pasta. SiTration’s technology has garnered interest from industry players, including mining giant Rio Tinto.
The core technology is based on work done at MIT to develop a novel type of membrane made from silicon, which is durable enough to withstand harsh chemicals and high temperatures while conducting electricity. It’s also highly tunable, meaning it can be modified or adjusted to suit different conditions or target specific materials.
SiTration’s technology also incorporates electro-extraction, a technique that uses electrochemistry to further isolate and extract specific target materials. This powerful combination of methods in a single system makes it more efficient and effective at isolating and recovering valuable materials, Smith says. So depending on what needs to be separated or extracted, the filtration and electro-extraction processes are adjusted accordingly.
Flexshuttle automated formulation laboratory
LLMatDesign: Autonomous Materials Discovery with Large Language Models
Discovering new materials can have significant scientific and technological implications but remains a challenging problem today due to the enormity of the chemical space. Recent advances in machine learning have enabled data-driven methods to rapidly screen or generate promising materials, but these methods still depend heavily on very large quantities of training data and often lack the flexibility and chemical understanding often desired in materials discovery. We introduce LLMatDesign, a novel language-based framework for interpretable materials design powered by large language models (LLMs). LLMatDesign utilizes LLM agents to translate human instructions, apply modifications to materials, and evaluate outcomes using provided tools. By incorporating self-reflection on its previous decisions, LLMatDesign adapts rapidly to new tasks and conditions in a zero-shot manner. A systematic evaluation of LLMatDesign on several materials design tasks, in silico, validates LLMatDesign’s effectiveness in developing new materials with user-defined target properties in the small data regime. Our framework demonstrates the remarkable potential of autonomous LLM-guided materials discovery in the computational setting and towards self-driving laboratories in the future.
Robotic lab proves new recipes make cleaner battery materials
Semiconductor advancements: Elastic strain ‘map’ guides the fine-tuning of material properties
If manufacturers are to meet the demand for semiconductors and improved computing performance, new materials and system structures must be identified and implemented. Elastic strain engineering (ESE) may help address this need. In contrast to doping, which tunes a semiconductor’s properties by adding trace amounts of other elements into the material, ESE tunes a material’s properties solely through the introduction of controlled mechanical strain. This method can be an easier way to tune the properties of wide-bandgap semiconductors, such as diamond, which are difficult to dope.
In February 2024, the researchers published their latest paper on the topic. In contrast to previous studies, which focused on answering specific open questions in the field, the new open-access paper took that knowledge and created a general “map” showing how to tune crystalline materials to produce specific thermal and electronic properties. The map, which was created using a combination of first principles calculations and machine learning, plots the stability regions of a crystal in six-dimensional strain space. Looking at the map reveals the conditions under which a material can exist in a particular phase and when it might fail or transition to another phase.
Carbonova Corp. Raised $6 Million to Produce Sustainable Materials from Greenhouse Gas Emissions
Carbonova Corp., a start-up that aims to turn greenhouse gas emissions into carbon materials for sustainable and inexpensive everyday essentials, announced that it has successfully closed its SAFE equity financing in an oversubscribed round with $6 million raised. The Company intends to use the net proceeds from the financing to advance its strategy towards building the first commercial demonstration carbon nanofibers unit in Canada.
The financing round was led by Kolon Industries, a multi-billion-dollar Korean conglomerate. Kolon has a keen interest in Carbonova’s technology applications in Asia, including batteries, plastics, and other materials. Another major participant in this round was the Natural Gas Innovation Fund NGIF Capital, a venture capital firm focused on innovative technologies for improving the environmental performance of existing or renewable natural gas and hydrogen production. This round also saw strong participation from the company’s directors, management, and staff team.
Carbonova currently produces carbon nanomaterials for customers at a pilot facility at the company’s headquarters in northeast Calgary, Alberta. The commercial demonstration expansion will result in unit production cost efficiencies and is forecast to reduce the CO2 footprint of the carbon nanomaterials to below net-zero.
Cellexcel raises over £250,000 to accelerate commercialisation of unique carbon emission-reducing biomaterial technology
Innovative biomaterial technology start-up Cellexcel has announced that it has closed its first funding round, raising over £250,000. Investments have come from College Green Ventures, New Anglia Capital, Low Carbon Innovation Fund, and Turquoise alongside angel investors from Anglia Capital Group. The funding will enable Cellexcel to engage with industry partners to further develop and commercialise Cellexcellent™ in real-world applications, while building its capabilities to enhance biomaterial water resistance.
Based on research from the University of East Anglia (UEA), Cellexcel has created a Cellexcellent™, a unique, patented technology to enhance the water resistance of biomaterials. This enables them to be integrated into external applications, such as composite panels used in the automotive or aerospace industry, reducing both weight and embedded CO2 emissions. By replacing emission-heavy materials such as polycarbonates, metal, or fibreglass composites companies benefit from around a 90% reduction in manufacturing CO2, – all while retaining their form, fit and function over time.
Using AI to discover stiff and tough microstructures
Innovative AI system from MIT CSAIL melds simulations and physical testing to forge materials with newfound durability and flexibility for diverse engineering uses.
A team of researchers moved beyond traditional trial-and-error methods to create materials with extraordinary performance through computational design. Their new system integrates physical experiments, physics-based simulations, and neural networks to navigate the discrepancies often found between theoretical models and practical results. One of the most striking outcomes: the discovery of microstructured composites — used in everything from cars to airplanes — that are much tougher and durable, with an optimal balance of stiffness and toughness.
Rounding the system out was their “Neural-Network Accelerated Multi-Objective Optimization” (NMO) algorithm, for navigating the complex design landscape of microstructures, unveiling configurations that exhibited near-optimal mechanical attributes. The workflow operates like a self-correcting mechanism, continually refining predictions to align closer with reality.
Discoveries in weeks, not years: How AI and high-performance computing are speeding up scientific discovery
Scientists at PNNL are testing a new battery material that was found in a matter of weeks, not years, as part of the collaboration with Microsoft to use advanced AI and high-performance computing (HPC), a type of cloud-based computing that combines large numbers of computers to solve complex scientific and mathematical tasks.
As part of this effort, the Microsoft Quantum team used AI to identify around 500,000 stable materials in the space of a few days. The new battery material came out of a collaboration using Microsoft’s Azure Quantum Elements to winnow 32 million potential inorganic materials to 18 promising candidates that could be used in battery development in just 80 hours. Most importantly, this work breaks ground for a new way of speeding up solutions for urgent sustainability, pharmaceutical and other challenges while giving a glimpse of the advances that will become possible with quantum computing.
Chemicals and materials to play key role in chips as 2-nm milestone nears
James O’Neill, chief technology officer of American chip material maker Entegris, said it is no longer the chipmaking machines, but advanced materials and cleaning solutions that are taking center stage in making advanced production processes possible. “Thirty years ago, it was all about lithography [equipment] to make [transistors on chips] smaller and improve device performance,” O’Neill said. Lithography refers to the key chipmaking process in which integrated circuits are printed onto a chip. How finely a machine can print these circuits generally defines how advanced the chips are.
Kai Beckmann, CEO of Merck’s electronics business, echoed that sentiment. “We are moving from the past two decades where [chipmaking] tools are most important to advance technologies to the next decade, what our customers call the age of materials,” Beckmann said. “The tools are still important, but now materials make all the difference.”
The jump to 2-nm production for logic chips, for example, required a completely new chip architecture. In this new configuration, referred to as gate-all-around (GAA), transistors are stacked in a more complex, three-dimensional way than in earlier, planar configurations. Developing the materials for new transistor configurations like gate-all-around requires innovative materials “that will coat the top, the bottom and the sides equally,” O’Neill said, adding that the industry is engineering ways to do this “at atomic scale dimensions.”
Merck’s Beckmann gave another example of the industry’s material evolution: Copper is widely used as a conductive layer in current chipmaking processes, but to make ever smaller and more advanced chips, the industry is exploring new materials such as molybdenum.
Multiple Rounds of In Vivo Random Mutagenesis and Selection in Vibrio natriegens Result in Substantial Increases in Rare Earth Element Binding Capacity
Rare earth elements (REE) are essential ingredients to many technologies including catalysts, high-efficiency lighting, and lightweight high-strength magnets found in hard drives, wind turbines, electric vehicles, and many other applications. These magnets often utilize multiple REE such as neodymium, praseodymium, dysprosium, and terbium. The demand for these technologies is rapidly increasing, and the corresponding supply of REE needs to increase with it. Current methods of purifying REE utilize solvent extraction, which often requires high temperatures and harsh chemicals, giving these important elements a high carbon and environmental footprint.
Adsorption, or biosorption, of REE onto bacterial cell membranes offers a sustainable alternative to traditional solvent extraction methods. But in order for biosorption-based REE purification to compete economically, the capacity and specificity of biosorption sites must be enhanced. Although there have been some recent advances in characterizing the genetics of REE-biosorption, the variety and complexity of bacterial membrane surface sites make targeted genetic engineering difficult. Here, we propose using multiple rounds of in vivo random mutagenesis induced by the MP6 plasmid combined with plate-throughput REE-biosorption screening to improve a microbe’s capacity and selectivity for biosorbing REE.
SonoPrint: Acoustically Assisted Volumetric 3D Printing for Composites
Advancements in additive manufacturing in composites have transformed various fields in aerospace, medical devices, tissue engineering, and electronics, enabling fine-tuning material properties by reinforcing internal particles and adjusting their type, orientation, and volume fraction. This capability opens new possibilities for tailoring materials to specific applications and optimizing the performance of 3D-printed objects. Existing reinforcement strategies are restricted to pattern types, alignment areas, and particle characteristics. Alternatively, acoustics provide versatility by controlling particles independent of their size, geometry, and charge and can create intricate pattern formations. Despite the potential of acoustics in most 3D printing, limitation arises from the scattering of the acoustic field between the polymerized hard layers and the unpolymerized resin, leading to undesirable patterning formation. However, this challenge can be addressed by adopting a novel approach that involves simultaneous reinforcement and printing the entire structure. Here, we present SonoPrint, an acoustically-assisted volumetric 3D printer that produces mechanically tunable composite geometries by patterning reinforcement microparticles within the fabricated structure. SonoPrint creates a standing wave field that produces a targeted particle motif in the photosensitive resin while simultaneously printing the object in just a few minutes. We have also demonstrated various patterning configurations such as lines, radial lines, circles, rhombuses, quadrilaterals, and hexagons using microscopic particles such as glass, metal, and polystyrene particles. Furthermore, we fabricated diverse composites using different resins, achieving 87 microns feature size. We have shown that the printed structure with patterned microparticles increased their tensile and compression strength by ∼38% and ∼75%, respectively.
Spirit AeroSystems, Oak Ridge National Laboratory Sign Memorandum of Understanding to Create Strategic Partnership
Spirit AeroSystems, Inc. today announced a strategic agreement with the Oak Ridge National Laboratory Manufacturing Demonstration Facility, which is managed by University of Tennessee Battelle, for the development of applications hypersonic travel and aircraft of tomorrow.
Spirit and Oak Ridge National Laboratory will jointly focus on scalable, efficient manufacturing of advanced material solutions in the commercial, defense and space aerostructure markets. They will collaboratively explore advances in high temperature in-situ process monitoring techniques and predictive modeling capability for microstructure-based performance and certification of carbon and ceramic composites as well as additively manufactured alloys. In addition, research teams will study various processing techniques for materials that can withstand extreme heat and harsh environments, including the scaling up of a thermal protection system for aerospace platforms.
Inferring material properties from FRP processes via sim-to-real learning
Fiber reinforced polymers (FRP) provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold. Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties (fiber volume content and permeability in X and Y direction) presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments and compare CNNs, ConvLSTMs, and Transformers. Moreover, we show how simulation-to-real transfer learning can improve a digital twin in FRP manufacturing, compared to simulation-only models and models based on sparse real data. The overall best metrics are: IOU 0.5031 and Accuracy 95.929 %, obtained by pretrained transformer models.
A New Type of Glass Promises to Cut Glass Manufacturing's Carbon Footprint in Half
The invention, known as LionGlass and engineered by researchers at Penn State, needs considerably less energy to produce and is highly damage-resistant compared to the standard soda lime silicate glass. The research group has filed a patent application as an initial step toward bringing the product to market.
Mauro believes that the enhanced strength of LionGlass means that the products made from it could be lighter in weight. Since LionGlass is 10 times more damage resistant compared to present glass, it could be considerably thinner.
3D Printing Materials Explained: Compare FDM, SLA, and SLS
Chemix Brings Transformative AI Technologies to EV Battery Industry, Launching the First AI-Designed Battery in 2023
Chemix, the startup leveraging AI to rapidly build high-performance and environmentally sustainable EV batteries, unveiled MIX™, its AI-powered design platform specifically developed to accelerate the commercialization of next-generation EV batteries. By leveraging MIX, Chemix is poised to revolutionize the decades-old EV battery industry, similar to how AI has transformed drug discovery by accelerating pharmaceutical research and development. This will enable the pace of battery innovation to finally catch up with the ever-growing demand for better-performing, safer, and more sustainable EV batteries.
A battery is to an electric vehicle what a processor is to a computer – a critical technical component determining the entire system’s performance. Despite this central importance, the approach researchers have used to develop new battery materials and systems has remained largely unchanged for decades – until now. As opposed to the conventional method that relies on time- and labor-intensive processes for battery development and testing, Chemix adopts a revolutionary AI-based approach. This accelerates the discovery of the best battery materials, formulations, and recipes by leveraging large proprietary experimental datasets and cutting-edge proprietary algorithms. As a result, the company has created a vertically-integrated end-to-end battery development approach from scratch.
Chemix’s innovation combines battery-specific AI algorithms and their vast collected data to accelerate and optimize battery design – seamlessly integrated with the MIX platform. Chemix has used MIX to experimentally test over 2,000 unique battery material designs across more than 40 variations of commercially-relevant battery formats, accumulating nearly three million test cycles.
🖨️ The Transformative Power of Innovations in Additive Materials
The slow but steady ascent of additive manufacturing (AM) into mainstream production environments is changing how products of all kinds are designed, made, and delivered. The evolution of advanced materials is further elevating the industry by empowering end-use parts and products with improved physical properties for greater utilization at lower costs as well as faster delivery and less waste.
Many, if not all, of the most popular additive materials can be enhanced through refinement of polymer formulations and compounding processes. Highly specialized skills in controlling the morphology and particle crystallization are needed, requiring chemists and scientists to create and iterate new material formulas.
In the world of AM, breakthroughs in polymer innovations are being driven by the demand for more affordable, lighter and higher-modulus composites as well as the ability to print materials that previously were too difficult to integrate into additive processes Additionally, the incorporation of value-added attributes to existing polymers is ushering in a new class of engineered materials with special functionality, such as flame-retardant or resistant attributes; reinforced materials containing glass fiber, as well as mineral fillers, carbon fiber, or nanotubes.
The inclusion of conductive attributes also is on the rise to address Electrostatic Dissipative (ESD), EMI-shielded or electrically conductive materials. The need for lubricated materials also is vital to reduce part friction and wear, along with the addition of UV-stable materials to reinforce part longevity. Many of these attributes are designed to extend the usefulness of materials for traditional manufacturing and 3D-printing applications, and vice versa.
Closed-loop fully-automated frameworks for accelerating materials discovery
Our work shows that a fully-automated closed-loop framework driven by sequential learning can accelerate the discovery of materials by up to 10-25x (or a reduction in design time by 90-95%) when compared to traditional approaches. We show that such closed-loop frameworks can lead to enormous improvement in researcher productivity in addition to reducing overall project costs. Overall, these findings present a clear value proposition for investing in closed-loop frameworks and sequential learning in materials discovery and design enterprises.
World-First Project to 'Self Heal' Cracked Concrete Using Sloppy Sludge Could Save $1.4 Billion Annual Repair Bill to Australia’s Sewer Pipes
A world-first project led by University of South Australia sustainable engineering expert Professor Yan Zhuge is trialling a novel solution to halt unprecedented levels of corrosion in the country’s ageing concrete pipelines. Self-healing concrete, in the form of microcapsules filled with water treatment sludge, could be the answer.
Corrosive acid from sulphur-oxidising bacteria in wastewater, along with excessive loads, internal pressure and temperature fluctuations are cracking pipes and reducing their life span, costing hundreds of millions of dollars to repair every year across Australia.
“Sludge waste shows promise to mitigate microbial corrosion in concrete sewer pipes because it works as a healing agent to resist acid corrosion and heal the cracks,” Prof Zhuge says.
The role of temperature on defect diffusion and nanoscale patterning in graphene
Jesse said, “It heals locally, like the (fictitious) liquid-metal T-1000 in Terminator 2: Judgment Day.”
Graphene is of great scientific interest due to a variety of unique properties such as ballistic transport, spin selectivity, the quantum hall effect, and other quantum properties. Nanopatterning and atomic scale modifications of graphene are expected to enable further control over its intrinsic properties, providing ways to tune the electronic properties through geometric and strain effects, introduce edge states and other local or extended topological defects, and sculpt circuit paths. The focused beam of a scanning transmission electron microscope (STEM) can be used to remove atoms, enabling milling, doping, and deposition. Utilization of a STEM as an atomic scale fabrication platform is increasing; however, a detailed understanding of beam-induced processes and the subsequent cascade of aftereffects is lacking. Here, we examine the electron beam effects on atomically clean graphene at a variety of temperatures ranging from 400 to 1000 °C. We find that temperature plays a significant role in the milling rate and moderates competing processes of carbon adatom coalescence, graphene healing, and the diffusion (and recombination) of defects. The results of this work can be applied to a wider range of 2D materials and introduce better understanding of defect evolution in graphite and other bulk layered materials.
Simplifying the world of materials properties evaluation using AI
Mettler-Toledo, together with CSEM and ZHAW has developed AIWizard: An artificial intelligence (AI) option for their STARe software that will make it easier to interpret DSC curves for thermal analysis.
Currently, manufacturers have high expectations surrounding the performance of their materials. A sealing ring must not become brittle, a PET bottle cannot deform, and medications need to react within the body at exactly the right time. Across the material science domain, Mettler-Toledo’s dynamic Differential Scanning Calorimeter (DSC) has become an indispensable tool for many. Thermal analysis makes a valuable contribution from quality control to research and development of materials and chemical compounds.
These autonomous factories on satellites will produce materials in space that can’t be made on Earth
Bacon and cofounder-CEO Joshua Western want to take advantage of the unique conditions in space—the very low gravity and the fact that it’s an almost perfect vacuum—to make materials that can’t be made on Earth. Some new materials have already been produced on the International Space Station. A new type of fiber-optic cable, for example, is cloudy when it’s made on Earth because of gravity and impurities in the air, but crystal clear when made in space.
In space, it’s possible to manufacture new alloys that can be used to make bigger, stronger, turbines on aircraft, so planes use less fuel. On electric planes, new materials can make the electronic connections between batteries and the propeller motor more efficient, so the planes need less cooling equipment and can carry more passengers. Space factories are also well-suited to make better batteries for electric planes or cars. Wind turbines, for example, are more efficient the larger they are, but have to be made in pieces so they can be transported to a site for installation, and then held together with bolts. By making bolts that are stronger than what can be manufactured on Earth, it’s possible to develop a larger, more efficient wind turbine that can create more energy.
Machine-learning system accelerates discovery of new materials for 3D printing
The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses.
A material developer selects a few ingredients, inputs details on their chemical compositions into the algorithm, and defines the mechanical properties the new material should have. Then the algorithm increases and decreases the amounts of those components (like turning knobs on an amplifier) and checks how each formula affects the material’s properties, before arriving at the ideal combination.
The researchers have created a free, open-source materials optimization platform called AutoOED that incorporates the same optimization algorithm. AutoOED is a full software package that also allows researchers to conduct their own optimization.
Machine learning predictions of superalloy microstructure
Gaussian process regression machine learning with a physically-informed kernel is used to model the phase compositions of nickel-base superalloys. The model delivers good predictions for laboratory and commercial superalloys. Additionally, the model predicts the phase composition with uncertainties unlike the traditional CALPHAD method.
Matlantis, a versatile atomistic simulator powered by deep learning
Complex machine validations performed with multiphysics simulation
When new materials and methods are applied to manufacturing, it increases product complexity. But the benefits can be significant: Products are now lighter, smaller and more easily customizable to meet consumer demands. Multiphysics simulations enable machine builders to explore the physical interactions complex products encounter, virtually. It tracks interactive data of product performance, safety and longevity.
Using AI to Find Essential Battery Materials
KoBold’s AI-driven approach begins with its data platform, which stores all available forms of information about a particular area, including soil samples, satellite-based hyperspectral imaging, and century-old handwritten drilling reports. The company then applies machine learning methods to make predictions about the location of compositional anomalies—that is, unusually high concentrations of ore bodies in the Earth’s subsurface.
Leveraging AI and Statistical Methods to Improve Flame Spray Pyrolysis
Flame spray pyrolysis has long been used to make small particles that can be used as paint pigments. Now, researchers at Argonne National Laboratory are refining the process to make smaller, nano-sized particles of various materials that can make nano-powders for low-cobalt battery cathodes, solid state electrolytes and platinum/titanium dioxide catalysts for turning biomass into fuel.
Cell Phones, Sporting Goods, and Soon, Cars: Ford Innovates with “Miracle” Material, Powerful Graphene for Vehicle Parts
Graphene has recently generated the enthusiasm and excitement in the automotive industry for paint, polymer and battery applications.
Dubbed a “miracle material” by some engineers, graphene is 200 times stronger than steel and one of the most conductive materials in the world. It is a great sound barrier and is extremely thin and flexible. Graphene is not economically viable for all applications, but Ford, in collaboration with Eagle Industries and XG Sciences, has found a way to use small amounts in fuel rail covers, pump covers and front engine covers to maximize its benefits.
“A small amount of graphene goes a long way, and in this case, it has a significant effect on sound absorption qualities,” said John Bull, president of Eagle Industries. The graphene is mixed with foam constituents, and tests done by Ford and suppliers has shown about a 17 percent reduction in noise, a 20 percent improvement in mechanical properties and a 30 percent improvement in heat endurance properties, compared with that of the foam used without graphene.