Abstract: A system, computer program product and a method to predict an objective function based on a recipe, and generate, via a machine learning model, a plurality of proposed different recipes of battery materials for optimizing at least objective function. Instances of the different proposed recipes of battery materials are prepared and deposited into an electrochemical module by a robotic preparation module. A robotic testing module executes a plurality of formulation characteristic tests on each deposited recipe instance and updates the machine learning model with a result of at least one of the formulation characteristic tests.

Abstract: Methods, systems, and apparatuses for multichannel, matrix-based testing of electrochemical materials are described herein. The system provides an electrochemical testing apparatus that consists of a bottom block having a first array of N×M receiving wells, and a top block with a second array of N×M chambers, with each of the N×M chambers having an electrical connector. The method involves inserting, into a number of the first array of N×M receiving wells, one or more electrochemical compositions to be tested. The method then involves closing the top block onto the bottom block. When the top and bottom blocks are closed, the N×M receiving wells and N×M chambers are aligned, thereby forming N×M testing cells. Finally, the method involves measuring one or more properties of the electrochemical composition inserted in the plurality of the N×M receiving wells.