Abstract: A method comprising providing a training data set to a machine learning (ML) system, the training data set indicative of density functional theory (DFT) data for a plurality of materials, the DFT data representing atomic configurations for the plurality of materials, determining a fingerprint for the atomic configuration for each of the plurality of materials, inputting the fingerprints into a machine learning system, generating, with the machine learning system, electron charge density data for the plurality of materials, inputting the electron charge density data and the fingerprint into a machine learning system, and predicting, with the machine learning system, one or more DFT properties of the plurality of materials.

Abstract: A method for predicting polymer properties that can include converting chemical fragments from a plurality of first polymers into standardized data strings, separating each of the standardized data strings into one or more tokens, predicting, via a first machine learning algorithm, one or more tokens from each of the standardized data strings, computing, via a processor device, one or more unique fingerprints for each of the standardized data strings, and mapping, via a second machine learning algorithm, one or more properties of the plurality of first polymers and one or more properties of a plurality of second polymers to the one or more unique fingerprints.

Abstract: Disclosed herein is a polymer prediction system, comprising a deep learning neural network and a training dataset. The deep learning neural network can comprise: a property branch comprising two or more layers, each layer having a plurality of neurons; a polymer branch comprising two or more layers, each layer having a plurality neurons; and a merged layer including a concatenation operation, the concatenation operation configured to concatenate the property branch and the polymer branch. The training dataset can include a plurality of known polymers and a plurality of descriptors for each of the plurality of known polymers. Also disclosed herein are methods of using the same.

Abstract: An exemplary embodiment of the present disclosure provides a method of designing a polymer. The method can include: providing a set of polymer data; generating a set of polymer structures; providing one or more target properties for the polymer, predicting properties of each polymer structure of the set of polymer structures, and design considerations for the set of polymer structures; and selecting one or more polymer structures from the set of polymer structures, based at least in part, on the predicted properties of the polymer structures. The polymer data can include a set of monomer structures.

Abstract: Disclosed herein is a polymer prediction system, comprising a deep learning neural network and a training dataset. The deep learning neural network can comprise: a property branch comprising two or more layers, each layer having a plurality of neurons; a polymer branch comprising two or more layers, each layer having a plurality neurons; and a merged layer including a concatenation operation, the concatenation operation configured to concatenate the property branch and the polymer branch. The training dataset can include a plurality of known polymers and a plurality of descriptors for each of the plurality of known polymers. Also disclosed herein are methods of using the same.

Abstract: A system for a material simulation is disclosed. The system may receive an input structure for the material and identify a reference grid point for the material. The system may determine a scalar, vector, and tensorial component for the material. Further, the system may render the vector component and the tensorial component rotationally invariant. Next, the system may generate a first structure fingerprint for the material based on the scalar, vector component, and tensorial component. The system may map the structure fingerprint to stored atomic configurations and based on corresponding stored atomic configuration(s), may determine an approximate total electronic charge density and a plurality of approximate energy levels for the reference grid point. Based on the plurality of approximate energy levels, the system may determine a predictive local density of states for the reference grid point. The system may also generate and display a visual simulation of the material.