Abstract: Embodiments generate computer based models, e.g., computer aided design (CAD) models, of materials. One such embodiment selects at least one section of a model representing a unit of a material. In turn, at least one physical or chemical property of the model is estimated based upon a proposed modification to the selected at least one section of the model and a proposed modification to a remainder of the model representing the unit of material. This selecting and estimating is iterated until the estimated at least one physical or chemical property conforms to a user specification of the at least one physical or chemical property. In this way, such an embodiment creates a model of a subject material that conforms to user specified physical and chemical properties.

Abstract: Embodiments determine properties of a molecule in an environment. One such embodiment constructs one or more three-dimensional (3D) structure models that indicate positions of atoms of the molecule. For each of the constructed one or more 3D structure models: (i) a surface model is generated that represents the environment, where the surface model includes a plurality of segments and the generated surface model defines a relationship between the indicated positions of the atoms of the 3D structure model and the plurality of segments and (ii) using a machine learning model, charge (e.g., electric charge) and chemical potential of each segment of the plurality of segments are predicted based on the 3D structure model and the generated surface model. An embodiment further predicts, using a supplemental machine learning model, energy corresponding to the 3D structure model based on the 3D structure model and the generated surface model.

Abstract: A pseudo micro computed tomography (CT-like) image of a porous material is produced. A chemistry-based 3D structure of a porous material system is generated, and a Connolly surface for the 3D structure is determined. A volume field of the 3D chemistry-based structure is calculated from the Connolly surface. A text-format file layer having layer by layer information of the volume field is generated. The text-format layer file is converted into a CT-like binary image file in the RAW format. The binary image file is converted to a black and white or grayscale images. A pore size analysis (PSA) simulation is performed to produce grain images and pore images for the porous material system.

Abstract: Embodiments calculate birefringence of materials. One such embodiment builds one or more three-dimensional structure models of one or more compounds forming a material. Each built three-dimensional structure model is aligned along a molecular axis and one or more tilt angles are set for each aligned three-dimensional structure model. A molecular polarizability tensor for each three-dimensional structure model with the set tilt angles is then calculated. An embodiment accounts for anisotropy by measuring the width and length of each model with the set tilt angles to determine aspect ratios. To continue, birefringence of the material is calculated based on the determined molecular polarizability tensors of the one or more models. Embodiments can be employed for simulating, optimizing, and designing real-world objects, e.g., in an optimization to select a material for a phone display that conforms with performance/manufacturing requirements.

Abstract: Embodiments calculate birefringence of materials. One such embodiment builds one or more three-dimensional structure models of one or more compounds forming a material. Each built three-dimensional structure model is aligned along a molecular axis and one or more tilt angles are set for each aligned three-dimensional structure model. A molecular polarizability tensor for each three-dimensional structure model with the set tilt angles is then calculated. An embodiment accounts for anisotropy by measuring the width and length of each model with the set tilt angles to determine aspect ratios. To continue, birefringence of the material is calculated based on the determined molecular polarizability tensors of the one or more models. Embodiments can be employed for simulating, optimizing, and designing real-world objects, e.g., in an optimization to select a material for a phone display that conforms with performance/manufacturing requirements.

Abstract: An automated system and method to investigate degradation of cathode materials in batteries via atomistic simulations, and in particular by simulating the creation of atomistic defects in the cathode material, which occurs during charge cycling. A systematic procedure relates the degradation of battery performance metrics to underlying structural changes due to atomic rearrangements within the material, for example through density functional theory simulations. The performance metrics modeled with this approach include the Open Cell Voltage (OCV) as well as the discharge capacity curve.

Abstract: Embodiments generate computer based models, e.g., computer aided design (CAD) models, of materials. One such embodiment selects at least one section of a model representing a unit of a material. In turn, at least one physical or chemical property of the model is estimated based upon a proposed modification to the selected at least one section of the model and a proposed modification to a remainder of the model representing the unit of material. This selecting and estimating is iterated until the estimated at least one physical or chemical property conforms to a user specification of the at least one physical or chemical property. In this way, such an embodiment creates a model of a subject material that conforms to user specified physical and chemical properties.

Abstract: An automated system and method to investigate degradation of cathode materials in batteries via atomistic simulations, and in particular by simulating the creation of atomistic defects in the cathode material, which occurs during charge cycling. A systematic procedure relates the degradation of battery performance metrics to underlying structural changes due to atomic rearrangements within the material, for example through density functional theory simulations. The performance metrics modeled with this approach include the Open Cell Voltage (OCV) as well as the discharge capacity curve.