Embodiments determine models that predict release profiles of substances from material matrices. An embodiment constructs a simulation model of a release system based on experimental data such as imaging and determines a model predicting a release profile through use of an iterative process. The process iterates: (i) modifying parameters of the constructed simulation model based upon release system mechanistic characteristic data to correct a transport coefficient of the release system and (ii) performing a simulation of the release system using the constructed simulation model with the modified parameters to generate a simulation-based release profile, until a given simulation-based release profile that matches the release system characteristic data is identified. The constructed simulation model with the modified parameters used to generate the matching simulation-based release profile is set as the model predicting the release profile of the substance from the material matrix.

The composition tonality determination method includes:

inputting at least one volatile molecule digital identifier, the volatile molecule digital identifier representative of a fragrant volatile molecule, the input defining a formula,

calculating, by a computing system, for at least one volatile molecule digital identifier of the formula, a value representative of an impact of each molecule on an activity level of an odorant receptor, represented by an odorant receptor digital identifier, each volatile molecule digital identifier being associated with at least one odorant receptor digital identifier, the association being a many-to-many association, and

determining, by a computing system, for the formula and as a function of at least one odorant receptor activity level impact calculated and a value representative of an odorant receptor activation threshold, a value representative of at least one tonality forming a composition, each odorant receptor digital identifier being associated with one tonality digital identifier, the association being a one-to-one association.

A method for setting conditions for use of a polymerization catalyst includes a step of acquiring a physical property value derived from remaining functional groups after maintaining a temperature of a composition including a polymerization-reactive compound and a predetermined amount of a polymerization catalyst, a step of calculating a remaining functional group ratio from the physical property value, a step of calculating a reaction rate constant based on a reaction rate equation from the remaining functional group ratio, a step of calculating an activation energy and a frequency factor from the reaction rate constant using an Arrhenius plot, a step of determining whether or not the activation energy satisfies a predetermined condition for the polymerization catalyst, an step of setting an approximation equation from the frequency factor, and a step of setting an addition range with respect to the polymerization-reactive compound.

This disclosure relates to application based design of novel materials. Conventional methods utilize laborious experimentation or costly first principles calculations. Conventional data driven techniques use point cloud-based representation for crystal structures, that suffers from permutation variance which is not inbuilt in a material's representation, the DL model has to learn invariance which may be inaccurate. Other methods use image based representation for crystal structures and separate images for each element type to represent the basis, which is memory and time intensive. Since each element is represented by its own image, it is difficult for model to learn chemical environment and neighborhood pattern of each element. The embodiments used image based representation of materials consistent with physical principles. Also, embodiments utilize elements matrix to obtain atoms and their positions from basis images. Thus, any material, irrespective of lattice geometry, and number and types of elements, is represented by only two images.

An optoelectronic system can include a single walled carbon nanotube (SWNT) device. The SWNT can include a carrier-doping density with optical conditions that control trion formation that respond via optical, electrical, or magnetic stimuli. The carrier-doping density can include a hole-polaron or electron-polaron concentration.

An optoelectronic system can include a single walled carbon nanotube (SWNT) device. The SWNT can include a carrier-doping density with optical conditions that control trion formation that respond via optical, electrical, or magnetic stimuli. The carrier-doping density can include a hole-polaron or electron-polaron concentration.

A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation.

A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation.

Effective compound feature quantities reflecting expert knowledge are efficiently generated to thereby accurately predict physical properties of an unknown compound with a device for predicting a material property using a case-by-case material database storing a plurality of case databases. The case databases include a plurality of records that record structural information about material structures in association with material properties about properties of materials. This device is includes a chemical space designation unit that receives a designation of at least one case database; an autoencoder learning unit that generates an autoencoder for converting structural information corresponding to the case database received by the chemical space designation unit to multi-variables; and a material property prediction unit that predicts material properties using the multi-variables converted by the autoencoder generated by the autoencoder learning unit.

Effective compound feature quantities reflecting expert knowledge are efficiently generated to thereby accurately predict physical properties of an unknown compound with a device for predicting a material property using a case-by-case material database storing a plurality of case databases. The case databases include a plurality of records that record structural information about material structures in association with material properties about properties of materials. This device is includes a chemical space designation unit that receives a designation of at least one case database; an autoencoder learning unit that generates an autoencoder for converting structural information corresponding to the case database received by the chemical space designation unit to multi-variables; and a material property prediction unit that predicts material properties using the multi-variables converted by the autoencoder generated by the autoencoder learning unit.