Toyota Research Institute, Inc.

Muratahan Aykol

Patrick Kenichi Herring

Abraham S Anapolsky

Santosh K Suram

Joseph Harold Montoya

Linda Hung

Jens Strabo Hummelshøj

Chirranjeevi Balaji Gopal

One aspect of the disclosure relates to systems and methods for determining probabilities of successful synthesis of materials in the real world at one or more points in time. The probabilities of successful synthesis of materials in the real world at one or more points in time can be determined by representing the materials and their pre-defined relationships respectively as nodes and edges in a network form, and computation of the parameters of the nodes in the network as input to a classification model for successful synthesis. The classification model being configured to determine probabilities of successful synthesis of materials in the real world at one or more points in time.

Methods for predicting likelihood of successful experimental synthesis of computer-generated materials by combining network analysis and machine learning

A synthesis machine for preparation of a targeted inorganic material for recommended synthesis by a computer program that determines optimal solid-state methods for synthesis of an inorganic material. The computational method involves inputting a target inorganic material, querying structural data and thermodynamic data for the target inorganic material, enumerating possible synthetic reactions to construct a synthetic reaction database with a viable subset of the possible synthetic methods. The routine generates a nucleation metric and competition metric that are combined to provide recommended synthetic methods. The output for each of the recommended syntheses are input into a robotic synthesis machine where the delivery of reactants, reaction conditions, and analysis of extent of reaction, and product quality is controlled by a processor.

Autonomous inorganic material synthesis machine

Methods are provided for tailoring multi-step chemical reactions having competing elementary steps using a strained catalyst. In various aspects, a layered piezo-catalytic system is provided, and may include a metal catalyst overlayer disposed on a piezo-electric substrate. The methods include applying a voltage bias to the piezo-electric substrate of the piezo-catalytic system resulting in a strained catalyst having an altered catalytic activity as a result of one or both of a compressive stress and tensile stress. The methods include exposing reagents for at least one step of the multi-step chemical reaction to the strained catalyst, and catalyzing the at least one step of the multi-step chemical reaction. In various aspects, the methods may include using an oscillating voltage bias applied to the piezo-electric substrate.

Dynamic piezocatalyst system

A computer system and computational method for determining optimal solid-state methods for synthesis of an inorganic material that results in an output of recommended synthetic methods that can be implemented based on the recommendation. The method involves inputting a target inorganic material, querying structural data and thermodynamic data for the target inorganic material and reactant inorganic materials that can be used for its synthesis, enumerating possible synthetic reactions to construct a synthesis reaction database with a viable subset of the possible synthetic methods. The program generates a nucleation barrier metric and a competition metric that are combined to provide a recommendation of the synthetic procedures to the target inorganic material.

Synthesis route recommendation engine for inorganic materials

Systems, methods, and storage media for generating a predicted discharge profile of a vehicle battery pack are disclosed. A method includes receiving, by a processing device, data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles operating under a plurality of conditions, the data received from at least one of each vehicle in the fleet of vehicles, providing, by the processing device, the data to a machine learning server, directing, by the processing device, the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, generating, by the processing device, the predicted discharge profile of the vehicle battery pack from the predictive model, and providing the discharge profile to an external device.

Systems, methods, and storage media for predicting a discharge profile of a battery pack

In one embodiment, a vehicle battery diagnostics system forecasts a future state for a battery by monitoring, over a period of time, one or more of voltage, current or temperature signals from at least one battery of the vehicle, storing information from the voltage, current or temperature signals as time-series data, obtaining a forecasting model from a server, the forecasting model indicating at least one shapelet feature that corresponds to a forecast categorization, identifying, in the time-series data, a shapelet that matches the at least one shapelet feature to a degree exceeding a predetermined similarity threshold, and providing a notification indicating the forecast categorization.

Vehicle battery analysis system

Systems, methods, and storage media for determining a target charging level of a battery pack for a drive route are disclosed. A method includes receiving data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle in the fleet of vehicles, providing the data to a machine learning server, directing the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, receiving a vehicle route request from the vehicle, the vehicle route request corresponding to the drive route, estimating travel conditions of the vehicle based on the route request, determining a temperature of the battery pack in the vehicle, determining a target battery charging level based on the predictive model, the travel conditions, and the temperature, and providing the target battery charging level to the vehicle.

Systems, methods, and storage media for determining a target battery charging level for a drive route

Compositions and process for optimizing oxygen reduction and oxygen evolution reactions are provided. Oxygen reduction and oxygen evolution catalysts include oxide compositions having a general formula a formula AMO, where x is electrochemically tuned to find optimal A content that delivers the best catalytic performance in a chemical system. The process provides the ability to find the optimal catalytic performance by tuning A and hence, the binding strength of O.

Compositions and processes for optimizing oxygen reduction and oxygen evolution reactions

Systems, methods, and storage media for arranging a plurality of cells in a vehicle battery pack are disclosed. A method includes receiving, by a processing device, data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle in the fleet and one or more battery testing devices, providing, by the processing device, the data to a machine learning server, directing, by the processing device, the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, estimating, by the processing device, one or more electrical characteristics of each cell to be included in the vehicle battery pack based on the predictive model, and directing, by the processing device, an arrangement of the cells within the battery pack based on the electrical characteristics.

Systems, methods, and storage media for arranging a plurality of cells in a vehicle battery pack

Systems, methods, and storage media for optimizing performance of a vehicle battery pack are disclosed. A method includes receiving data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles, the data received from at least one of each vehicle, providing the data to a machine learning server, and directing the machine learning server to generate a predictive model. The predictive model is based on machine learning of the data. The method further includes providing the predictive model to each vehicle, the predictive model providing instructions for adjusting configuration parameters for each of the cells in the battery pack such that the battery pack is optimized for a particular use, and directing each vehicle to optimize performance of the vehicle battery pack based on the predictive model.

Systems, methods, and storage media for adapting machine learning models for optimizing performance of a battery pack

An existing materials database (EMDB) is a compilation of inorganic materials with composition and crystal structures known from prior experimental synthesis and characterization reports, or from ab initio or other computational studies, and includes a composition, structure, and stability value for each material. A hypothetical materials database (HMDB) is an extremely large compilation of materials of unknown stability and synthesizability, with no explicitly available or accessible prior experimental or computational report of their structure-composition combinations. An automated process for efficiently expanding the size of an EMDB includes a cyclical sub-process in which a rapid algorithm provides preliminary stability estimates for hypothetical materials selected from an HMDB, and those materials with a favorable stability prediction undergo a full ab initio analysis to obtain quantitative stability values and are then added to the EMDB. During each iteration of the cyclical sub-process, the rapid algorithm is trained on the EMDB, so that it becomes more effective at providing preliminary stability estimates with each iteration, as the EMDB expands.

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Santa Clara, CA, United States of America

Muratahan Aykol