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.
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.