Abstract: A simulation device includes a determination unit configured to compute a distance between an atom forming a first molecule and an atom forming a second molecule while performing a molecular dynamics computation, and determine whether or not the first molecule and the second molecule are in close proximity of each other, an imparting unit configured to impart a parameter for increasing a reaction probability with respect to the first molecule and/or the second molecule in a case where the determination unit determines that the first molecule and the second molecule are in close proximity of each other, an elementary reaction generation unit configured to generate an elementary reaction in a transition state, based on a chemical reaction identified in the molecular dynamics computation, and a reaction rate constant computation unit configured to compute a reaction rate constant of the generated elementary reaction.

Abstract: A computer is caused to function as a condition storage unit configured to store one or more conditions represented by coordinates in a multi-dimensional space, a candidate condition generating unit configured to generate a plurality of candidate conditions represented by coordinates in the multi-dimensional space, a candidate condition selecting unit configured to calculate, for a coordinate position of each of the candidate conditions, a shortest distance to coordinate positions of the one or more conditions, and to select, as a coordinate position of a new condition, a coordinate position of one of the candidate conditions for which the shortest distance is longest, and a new condition adding unit configured to add the selected coordinate position of the new condition to the coordinate positions of the one or more conditions, wherein the candidate condition selecting unit repeats, until a termination condition is satisfied, a process of calculating, for a coordinate position of each of the candidate condit

Abstract: With respect to an information processing device that supports creation of an Ising model for causing an annealing-type optimization machine to solve an optimum solution search problem, the information processing device includes a transforming unit configured to binarize an explanatory variable included in a training data set created using a trained machine learning model; a training unit configured to train an Ising model by performing machine learning with a relationship between the binarized explanatory variable and a predicted value of the training data set; and an output unit configured to output the trained Ising model.

Abstract: Occurrence of waviness abnormality is predicted. A waviness prediction device includes a data input part configured to receive an input of waviness data obtained by measuring waviness of a polishing target surface that is polished by rotating a polishing pad with a planetary gear mechanism; a feature obtainment part configured to obtain a feature including both a number of processed polishing targets in the polishing pad, and a torque of a sun gear; and a waviness prediction part configured to predict the waviness by inputting the feature obtained by the feature obtainment part to a prediction model that learns a relationship between the feature and the waviness data.

Abstract: A prediction apparatus includes a trained model trained by using training data in which a material composition of a material to be learned is associated with a phase fraction of the material to be learned at each temperature within a predetermined temperature range, the trained model being configured to predict a phase fraction at an i+1-th temperature by using a phase fraction predicted by the trained model for one or more temperatures up to an i-th temperature within the predetermined temperature range (where i is an integer of 1 or more). The prediction apparatus is configured to input a material composition of a material to be predicted into the trained model, thereby predicting a phase fraction of the material to be predicted at each temperature within the predetermined temperature range.

Abstract: A material design device derives the optimal solution for a design condition satisfying a desired material property. A design condition setting unit for setting a specified range of a design condition of a material to be designed. A comprehensive prediction point generation unit generates a plurality of comprehensive prediction points within the specified range set by the design condition setting unit. A design condition-material property table stores data sets in which each point of the comprehensive prediction points is associated with a material property value calculated by inputting the comprehensive prediction points generated by the comprehensive prediction point generation unit, to a learned model. A required property setting unit sets a specified range of a required property of the material. A design condition extraction unit extracts, from the design condition-material property table, a data set satisfying the required property set by the required property setting unit.

Abstract: A composition search method for a material includes constructing a prediction model by learning training data in which information related to a composition of a material is set as an explanatory variable and a value of a physical property of the material is set as an objective variable; calculating a predicted value of the physical property by inputting, into the prediction model, prediction data for newly searching for a composition; calculating an influence degree of each explanatory variable on prediction by using the training data and the prediction model; calculating a weighted distance of the prediction data with respect to the training data by using the influence degree; and displaying a relationship between the predicted value and the weighted distance, and outputting corresponding prediction data as a search candidate.

Abstract: An information processing device for assisting creation of an Ising model receives an objective function that formulates characteristics of a composite material according to a formulation and a condition of constraint on the formulation, and creates the Ising model to solve an optimal solution of the formulation that satisfies a constraint condition expression and optimizes the characteristics. The condition of constraint on the formulation includes a condition related to a mix number of substances included in a substance group and to be mixed in the composite material, for each substance group composing the composite material, and a condition related to an amount of substance to be mixed in the composite material, for each substance included in the substance group.

Abstract: Prediction accuracy of trained prediction models is improved by setting an appropriate weight using a trained clustering model and classified clusters. A method of generating a model for predicting a material characteristic includes a step of acquiring a training dataset, a step of generating a trained clustering model using the training dataset and a clustering model, and classifying the training dataset into N clusters, a step of calculating a distance between centroids of the clusters, a step of calculating a weight between the clusters using the distance between the centroids of the clusters and a parameter representing a feature of the training dataset, and a step of generating, for the N clusters, respective trained prediction models {Mi} 1?i?N using the clusters and the weight.

Abstract: A prediction data display device includes a processor configured to input, into a trained model obtained by performing a training process on a model with training data including information on first characteristic data indicating a deterioration degree of a first consumable in a first period as input data and information on second characteristic data indicating a deterioration degree of the first consumable in a second period after the first period as ground-truth data, information on third characteristic data indicating a deterioration degree of a second consumable in the first period to calculate information on fourth characteristic data indicating a deterioration degree of the second consumable in the second period; and display a graph of the fourth characteristic data and a graph of the second characteristic data with a display appearance determined in accordance with a similarity between the information on the first characteristic data and the third characteristic data.

Abstract: A property prediction device includes a processor; and a memory storing program instructions that cause the processor to: create a prediction model by using a training dataset of a composite material including raw materials in first and second categories to perform machine learning of a correspondence relationship between a property of the composite material, which is an objective variable, versus a mixing amount of the raw material in the first category and a weighted feature of the raw material in the second category, which are explanatory variables; and input, as explanatory variables, a mixing amount of a raw material in the first category and a weighted feature of a raw material in the second category, created based on prediction data of a composite material whose property is to be predicted, into the prediction model so as to predict the property of the composite material corresponding to the prediction data.

Abstract: An information processing apparatus for receiving input of information on a material composition from an operator includes a material reception unit configured to receive selection of a material, to which input of information is directed, in accordance with an operation specifying an on-screen position performed by the operator, and a material-content reception unit configured to receive selection of a content of the material, to which the input of information is directed, in accordance with an operation stopping specifying the on-screen position.

Abstract: An evaluation apparatus, an evaluation method, and an evaluation program applicable to a quality evaluation of sintered bodies are provided. The evaluation apparatus includes: an acquisition unit configured to acquire a cross-sectional image obtained by photographing a cross-section of a sintered body group stained with a staining solution; a generation unit configured to extract a saturation component for each of sintered bodies from the cross-sectional image, thereby to generate a saturation component image; and a visualization unit configured to visualize the saturation component image.

Abstract: A material design support apparatus for supporting optimization of a design condition for a material, the material design support apparatus including: a design condition setting unit configured to set a range of a design condition for a material; a required characteristic setting unit configured to set a range of a required characteristic of the material; an exhaustive prediction point generation unit configured to generate a plurality of exhaustive prediction points within the range of the design condition; a prediction unit configured to input the exhaustive prediction points into a trained model in which a correspondence between a design condition for the material and a characteristic value of the material is learned, thereby to predict a characteristic value of the material; and a design condition adjustment unit configured to adjust a range of the design condition for the material in which a plurality of exhaustive prediction points are subsequently generated.

Abstract: Technical knowledge which is difficult for the expert to express in words or numerical form is predicted and reproduced. A technical knowledge prediction apparatus according to one embodiment of the present invention includes a problem setting unit configured to automatically create and set a problem which includes a control factor of a manufacturing step, and a variable of the control factor, an answer acquiring unit configured to acquire an answer with respect to the problem and indicating a decision of the manufacturing step, a query acquiring unit configured to acquire a query, and a prediction output unit configured to predict and output an answer corresponding to the query acquired by the query acquiring unit, based on a corresponding relationship between the problem and the answer.

Abstract: To improve accuracy in a document search for a document that includes a typographical error. A document retrieval device according to one embodiment of the present invention includes a misidentification table storing a correctly identified character string and a misidentified character string. The document retrieval device includes a document searcher. The document searcher obtains a search character string, and retrieves the search character string from both a document and a character string that is obtained by changing the misidentified character string included in the document to the correctly identified character string.

Abstract: A document search device includes a processor, and a memory storing program instructions that cause the processor to search for an input keyword in a document database in which document information including text data extracted by using a character recognition process from document image data generated by imaging a paper document is stored, select a similar keyword in accordance with a degree of similarity to the input keyword from a group of wildcard strings generated from the input keyword and search for the similar keyword in the document database, the degree of similarity being determined by comparing each character of the input keyword with a corresponding character of a wildcard string in the group of wildcard strings, and output a search result obtained by searching for the input keyword in the document database and a search result obtained by searching for the similar keyword in the document database.

Abstract: A physical property prediction device for predicting a physical property of a compound including: a generation unit configured to generate a first stage trained model, by using, as training data, first stage synthesis information and synthesis result information; a generation unit configured to generate second stage through last stage trained models, by using, as training data, n-th stage (n?2) synthesis information, n-th stage synthesis result information, and n?1-th stage synthesis result information; a reception unit configured to receive a synthesis information setting for the compound for which the physical property is predicted; a prediction unit configured to predict a physical property value of a product synthesized by the first stage chemical reaction of the compound; a prediction unit configured to repeat, from a second stage chemical reaction to a last stage chemical reaction of the compound, a process of predicting a physical property value of a product synthesized by the n-th stage chemical react

Abstract: An image analysis apparatus according to an embodiment of the present invention includes: a processor; and a memory storing program instructions that cause the processor to: determine a shape of a particle included in a particle image that is extracted from an image of an object, so that an OK particle image which is a particle image of an OK particle that satisfies a predetermined standard for shape and a provisional NG particle image which is a particle image of a provisional NG particle that does not satisfy the predetermined standard, are obtained; generate a pseudo image using a generative model; and determine whether the provisional NG image and the pseudo image are similar, wherein in a case where the provisional NG image and the pseudo image are determined to be similar, the provisional NG particle is determined as including an OK particle.

Abstract: Prediction accuracy is increased in a prediction device using a trained model. A prediction device includes: a first trained model and a second trained model configured to respectively output first output data and second output data in response to input of input data of a prediction target; and an output portion configured to obtain the first output data and the second output data and calculate a weighted average value or take a weighted majority, thereby outputting prediction data. The first trained model is configured such that prediction accuracy for the input data of an interpolation region becomes higher than in the second trained model. The second trained model is configured such that prediction accuracy for the input data of an extrapolation region becomes higher than in the first trained model.