Abstract: A method for neural network material state prediction is described. The method includes encoding a sequence and interrelationships among events occurring in a simulation and/or experiment in an event-sourced architecture for materials provenance (ESAMP) framework. The method also includes learning an initial state of a material sample in the ESAMP framework. The method further includes sharing a state vector representing the initial state of the material sample with other material samples in the ESAMP framework. The method also includes learning how one or more processes affect the state of the material sample in the ESAMP framework according to the state vector shared with the other material samples in the ESAMP framework.

Abstract: A method for setting parameters for a chemical reaction includes receiving a target material and a set of available substrate structures to host the target material. The method also includes filtering stored thermodynamic parameters associated with chemical compounds based on receiving the target material and the set of available substrate structures. The method further includes generating a recommendation indicating a precursor electrolyte, a substrate structure from the set of available substrate structures, and electrochemical conditions for obtaining the target material, the recommendation may be generated based on filtering the stored thermodynamic parameters.