Abstract: A method for rapid inverse design is disclosed which utilizes generative machine learning and additive manufacturing, which allows for metamaterial creations based on arbitrary stress-strain curves that characterize their mechanical behaviors. In embodiments, a user sketches a target compressive stress-strain curve described by a set of curve features as the input into a generative machine learning pipeline, which outputs an optimal digital design within a few seconds that, once 30 printed, will fully replicate the prescribed stress-strain curve upon compression testing. The present invention accounts for process-dependent errors from a mass-customer based printing technique and enables the rapid creation of nearly all possible stress-strain curve cases subjected to compression; and is able to achieve, exotic mechanical behavior along with high degrees of tailorability, with high fidelity between target and measured curves from 3D printed inverse-designed metamaterials.

Abstract: Artificial intelligence and machine learning models are used to make concrete-related predictions. Many permutations of concrete mixtures are generated. Machine learning algorithms are used to evaluate and recommend a generated concrete mixture based on a set of specifications. The generated concrete mixture can be sent to a plant for production. The actual concrete mixture that was used to manufacture the concrete product can be received from the manufacturer. An amount of emission reductions and/or cost savings can be determined from the actual as-batched concrete mixture and an associated reference concrete mixture. The real-world data are used to train the machine learning models.

Abstract: Provided herein are assemblies and methods for lithium, magnesium, and/or calcium extraction. In various embodiments, an assembly includes a leaching tank defining an interior chamber having one or more inlets and one or more outlets. The leaching tank is configured to receive through the one or more inlets a mixture comprising a substrate having one or more target metals and a solvent. The assembly further includes a sonic probe positioned within the interior chamber, suspended in the solution, and/or a sonic plate configured to provide sonic energy to the mixture.

Abstract: Artificial intelligence and machine learning models are used to make concrete-related predictions. Many permutations of concrete mixtures are generated. Machine learning algorithms are used to evaluate and recommend a generated concrete mixture based on a set of specifications. The generated concrete mixture can be sent to a plant for production. The actual concrete mixture that was used to manufacture the concrete product can be received from the manufacturer. An amount of emission reductions and/or cost savings can be determined from the actual as-batched concrete mixture and an associated reference concrete mixture. The real-world data are used to train the machine learning models.

Abstract: Artificial intelligence and machine learning models are used to make concrete-related predictions. Many permutations of concrete mixtures are generated. Machine learning algorithms are used to evaluate and recommend a generated concrete mixture based on a set of specifications. The generated concrete mixture can be sent to a plant for production. The actual concrete mixture that was used to manufacture the concrete product can be received from the manufacturer. An amount of emission reductions and/or cost savings can be determined from the actual as-batched concrete mixture and an associated reference concrete mixture. The real-world data are used to train the machine learning models.