An information processing method is performed by a computer for evaluating flammability of a mixed refrigerant material containing a plurality of components. The method includes: calculating, for each of the plurality of components, a second value obtained by multiplying a mixture ratio thereof in the mixed refrigerant material by a first value obtained based on numbers of hydrogen atoms, halogen atoms, and double bonds included in a molecular structure thereof; calculating a total sum of the second value calculated for each of the plurality of components; and classifying the mixed refrigerant material into a predetermined flammability class based on the total sum.

A method of generating a chemical structure performed by a neural network device includes receiving a target property value and a target structure characteristic value; selecting first generation descriptors; generating second generation descriptors; determining, using a first neural network of the neural network device, property values of the second generation descriptors; determining, using a second neural network of the neural network device, structure characteristic values of the second generation descriptors; selecting, from the second generation descriptors, candidate descriptors that satisfy the target property value and the target structure characteristic value; and generating, using the second neural network of the neural network device, chemical structures for the selected candidate descriptors.

A method of generating a chemical structure performed by a neural network device includes receiving a target property value and a target structure characteristic value; selecting first generation descriptors; generating second generation descriptors; determining, using a first neural network of the neural network device, property values of the second generation descriptors; determining, using a second neural network of the neural network device, structure characteristic values of the second generation descriptors; selecting, from the second generation descriptors, candidate descriptors that satisfy the target property value and the target structure characteristic value; and generating, using the second neural network of the neural network device, chemical structures for the selected candidate descriptors.

One aspect of the disclosure relates to systems and methods for determining probabilities of successful synthesis of materials in the real world at one or more points in time. The probabilities of successful synthesis of materials in the real world at one or more points in time can be determined by representing the materials and their pre-defined relationships respectively as nodes and edges in a network form, and computation of the parameters of the nodes in the network as input to a classification model for successful synthesis. The classification model being configured to determine probabilities of successful synthesis of materials in the real world at one or more points in time.

A material evaluation device includes one or more memories; and one or more processors coupled to the one or more memories and the one or more processors configured to: store a certain number of hysteresis curves that, with respect to a change in a first physical quantity of N times at least one of a plurality of positions of a material, each represents a change in a second physical quantity of each time, the N being an integer equal to or greater than 2, extract points extracted by scanning each of the N hysteresis curves with a value of the second physical quantity for at least one of the plurality of positions, generate one-dimensional information regarding the second physical quantity by arraying the extracted points, and acquire a physical property value of the material by using the generated one-dimensional information.

The present invention relates to a method for characterising, by means of an electronic nose, the release kinetics of odorous compounds from a sample, comprising the following series of steps: (a) supplying a sample; (b) at a time t1, exposing the sensor array of the electronic nose to some of the gaseous medium comprising the odorous compounds released from the sample, and processing the response emitted by the sensor array of the electronic nose, after said exposure, in the form of a signal; and (c) repeating step (b) at least once, at a time t2 different from the time t1, whereby an olfactory kinetic signature characterising the sample is obtained. The present invention also relates to the use of this method for anti-counterfeiting and/or quality control purposes and for generating a data bank or database of temporal olfactory signatures. The present invention finally relates to certain devices used when implementing such methods.

The present invention relates to a method for characterising, by means of an electronic nose, the release kinetics of odorous compounds from a sample, comprising the following series of steps: (a) supplying a sample; (b) at a time t1, exposing the sensor array of the electronic nose to some of the gaseous medium comprising the odorous compounds released from the sample, and processing the response emitted by the sensor array of the electronic nose, after said exposure, in the form of a signal; and (c) repeating step (b) at least once, at a time t2 different from the time t1, whereby an olfactory kinetic signature characterising the sample is obtained. The present invention also relates to the use of this method for anti-counterfeiting and/or quality control purposes and for generating a data bank or database of temporal olfactory signatures. The present invention finally relates to certain devices used when implementing such methods.

A material screening process of generating input features for each material of a subset of materials to be screened, generating target properties for each material of the subset of materials, inputting screening conditions, the input features, and the target properties into a material screening artificial intelligence model and training the material screening artificial intelligence model based on the inputs. Once the model is trained, inputting a dataset of materials to be screened into the trained material screening artificial intelligence model, the dataset of materials includes the subset of materials used to train the model, screening the dataset of materials on the trained material screening artificial intelligence model using the screening conditions and ranking the materials of the dataset based on predicted target properties obtained from the screening.

A material screening process of generating input features for each material of a subset of materials to be screened, generating target properties for each material of the subset of materials, inputting screening conditions, the input features, and the target properties into a material screening artificial intelligence model and training the material screening artificial intelligence model based on the inputs. Once the model is trained, inputting a dataset of materials to be screened into the trained material screening artificial intelligence model, the dataset of materials includes the subset of materials used to train the model, screening the dataset of materials on the trained material screening artificial intelligence model using the screening conditions and ranking the materials of the dataset based on predicted target properties obtained from the screening.

Embodiments relate to a system for predicting thermodynamic phase of a material. The system includes a phase diagram image scanning processing module configured to scan a binary phase diagram for each material to be used as a component of a high-entropy alloy (HEA). The system includes a feature computation processing module configured to generate a primary feature and an adaptive feature. The primary feature is representative of a probability that the HEA will exhibit a solid solution phase and/or an intermetallic phase. The adaptive feature is representative of a factor favoring formation of a desired intermetallic HEA phase. The system includes a prediction module configured to encode the primary feature and/or the adaptive feature with thermodynamic data associated with formation of HEA alloy phases to provide an output representation of the HEA alloy phases for a material under analysis.