Abstract: The present disclosure describes a system, method, and computer program for automatically discovering goals from conversations using neural networks and deep multi-view clustering. A dataset of conversations is partitioned into two views. Vector representations of each view are then generated and clustered in an alternating fashion between views for a number of iterations (i.e., the system alternates between views in generating and clustering vector representations of a view). A first neural network encoder generates the vector representations for the first view, and a second neural network encoder generates the vector representations for the second view. With each semi-iteration, cluster assignments from one view are used to update the encoder for the other view, thus encouraging the two neural network encoders to yield similar cluster assignments.

Abstract: A method of using data-driven predictive modeling to predict and classify battery cells by lifetime is provided that includes collecting a training dataset by cycling battery cells between a voltage V1 and a voltage V2, continuously measuring battery cell voltage, current, can temperature, and internal resistance during cycling, generating a discharge voltage curve for each cell that is dependent on a discharge capacity for a given cycle, calculating, using data from the discharge voltage curve, a cycle-to-cycle evolution of cell charge to output a cell voltage versus charge curve Q(V), generating transformations of ?Q(V), generating transformations of data streams that include capacity, temperature and internal resistance, applying a machine learning model to determine a combination of a subset of the transformations to predict cell operation characteristics, and applying the machine learning model to output the predicted battery operation characteristics.

Abstract: A method of probing a multidimensional parameter space of battery cell test protocols is provided that includes defining a parameter space for a plurality of battery cells under test, discretizing the parameter space, collecting a preliminary set of cells being cycled to failure for sampling policies from across the parameter space and include multiple repetitions of the policy, specifying resource hyperparameters, parameter space hyperparameters, and algorithm hyperparameters, selecting a random subset of charging policies, testing the random subset of charging policies until a number of cycles required for early prediction of battery lifetime is achieved, inputting cycle data for early prediction into an early prediction algorithm to obtain early predictions, inputting the early predictions into an optimal experimental design (OED) algorithm to obtain recommendations for running at least one next test, running the recommended tests by repeating from the random subset testing step above, and validating final

Abstract: Materials, designs, and methods of fabrication for iron-manganese oxide electrochemical cells are disclosed. In various embodiments, the negative electrode is comprised of pelletized, briquetted, or pressed iron-bearing components, including metallic iron or iron-based compounds (oxides, hydroxides, sulfides, or combinations thereof), collectively called “iron negative electrode.” In various embodiments, the positive electrode is comprised of pelletized, briquetted, or pressed manganese-bearing components, including manganese (IV) oxide (MnO2), manganese (III) oxide (Mn2O3), manganese (III) oxyhydroxide (MnOOH), manganese (II) oxide (MnO), manganese (II) hydroxide (Mn(OH)2), or combinations thereof, collectively called “manganese oxide positive electrode.” In various embodiments, electrolyte is comprised of aqueous alkali metal hydroxide including lithium hydroxide (LiOH), sodium hydroxide (NaOH), potassium hydroxide (KOH), cesium hydroxide (CsOH), or combinations thereof.

Abstract: Systems and methods of the various embodiments may provide metal electrodes for electrochemical cells. In various embodiments, the electrodes may comprise iron. Various methods may enable achieving high surface area with low cost for production of metal electrodes, such as iron electrodes.

Abstract: A method of probing a multidimensional parameter space of battery cell test protocols is provided that includes defining a parameter space for a plurality of battery cells under test, discretizing the parameter space, collecting a preliminary set of cells being cycled to failure for sampling policies from across the parameter space and include multiple repetitions of the policy, specifying resource hyperparameters, parameter space hyperparameters, and algorithm hyperparameters, selecting a random subset of charging policies, testing the random subset of charging policies until a number of cycles required for early prediction of battery lifetime is achieved, inputting cycle data for early prediction into an early prediction algorithm to obtain early predictions, inputting the early predictions into an optimal experimental design (OED) algorithm to obtain recommendations for running at least one next test, running the recommended tests by repeating from the random subset testing step above, and validating final

Abstract: A method of using data-driven predictive modeling to predict and classify battery cells by lifetime is provided that includes collecting a training dataset by cycling battery cells between a voltage V1 and a voltage V2, continuously measuring battery cell voltage, current, can temperature, and internal resistance during cycling, generating a discharge voltage curve for each cell that is dependent on a discharge capacity for a given cycle, calculating, using data from the discharge voltage curve, a cycle-to-cycle evolution of cell charge to output a cell voltage versus charge curve Q(V), generating transformations of ?Q(V), generating transformations of data streams that include capacity, temperature and internal resistance, applying a machine learning model to determine a combination of a subset of the transformations to predict cell operation characteristics, and applying the machine learning model to output the predicted battery operation characteristics.

Abstract: A system and method for integrating one or more external systems, where the integration is achieved through the collaboration of an engine and one or more adapters. The engine can be configured with a set of rules that defines how messages are distributed among the adapters, and is responsible for enforcing those rules. Each adapter is associated with an external system. Each adapter is responsible for translating messages received from the associated external system into a common messaging protocol and for transmitting the translated messages to the engine. In addition, each adapter is responsible for translating messages in the common messaging protocol received from the engine and for transmitting the translated messages to the associated external system. Engines and adapters may be distributed among several computers.