Traditionally, new alloy development and processing involved various high-end expansive experiments, huge development time and cost of required man-hours. One of the major issues, which limits the ability for materials scientists to design metallic materials from atoms using Molecular Dynamics (MD), is the lack of accurate interatomic molecular dynamics potentials (MDPs). Suitable MDPs of desired alloy systems enable new alloy compositions and related properties, but however, this is very difficult and time-consuming process. The present disclosure enables developing molecular dynamics potential for new/traditional metallic alloys for their simulated structural, thermodynamic, and mechanical property predictions. Present disclosure provides systems and methods for generating MDP for multi-element alloy systems wherein both Body Centered Cubic (BCC) element type and/or a Face Centered Cubic (FCC) element type are combined. Pure elements and multi-element alloys of combinations of BCC and FCC elements are modeled for predicting their various structural, thermodynamic, and mechanical properties.

Traditionally, new alloy development and processing involved various high-end expansive experiments, huge development time and cost of required man-hours. One of the major issues, which limits the ability for materials scientists to design metallic materials from atoms using Molecular Dynamics (MD), is the lack of accurate interatomic molecular dynamics potentials (MDPs). Suitable MDPs of desired alloy systems enable new alloy compositions and related properties, but however, this is very difficult and time-consuming process. The present disclosure enables developing molecular dynamics potential for new/traditional metallic alloys for their simulated structural, thermodynamic, and mechanical property predictions. Present disclosure provides systems and methods for generating MDP for multi-element alloy systems wherein both Body Centered Cubic (BCC) element type and/or a Face Centered Cubic (FCC) element type are combined. Pure elements and multi-element alloys of combinations of BCC and FCC elements are modeled for predicting their various structural, thermodynamic, and mechanical properties.

A computer implemented method for designing chemical reactions for catalyst construction is described. The method includes extracting historical data including historic chemical reaction data and historic catalyst construction yield data and converting the historic chemical reaction data into graph models to represent molecular structure data. The method also includes incorporating the graph models into a chemical reaction algorithm and training a vectorized cognitive deep learning network of the chemical reaction algorithm by using the graph models and a property of the historic chemical reaction data to produce a catalyst chemical reaction model. Further, the method includes validating the catalyst chemical reaction model by inputting the historic chemical reaction data and comparing a generated property corresponding to the catalyst chemical reaction model to the property of the historic chemical reaction data. Lastly, the method includes updating the training of the catalyst chemical reaction model.

A computer implemented method for designing chemical reactions for catalyst construction is described. The method includes extracting historical data including historic chemical reaction data and historic catalyst construction yield data and converting the historic chemical reaction data into graph models to represent molecular structure data. The method also includes incorporating the graph models into a chemical reaction algorithm and training a vectorized cognitive deep learning network of the chemical reaction algorithm by using the graph models and a property of the historic chemical reaction data to produce a catalyst chemical reaction model. Further, the method includes validating the catalyst chemical reaction model by inputting the historic chemical reaction data and comparing a generated property corresponding to the catalyst chemical reaction model to the property of the historic chemical reaction data. Lastly, the method includes updating the training of the catalyst chemical reaction model.

An automated chemical formulation apparatus includes: a data reception unit that receives an input chemical material dataset including chemical material information, chemical composition information, chemical formulation information and property information thereof; a predicting model generation unit that trains a first machine learning model using the input chemical material dataset to generate a predicting model for predicting a chemical formulation based on target property information of a target material; and a formulation prediction unit that sets a boundary condition based on the input chemical material dataset, generates a new input dataset including at least one of chemical material information, chemical composition information, and chemical formulation information within the boundary condition, inputs the new input dataset to the predicting model, and sets predetermined one or more pieces of target property information to perform prediction, thereby outputting a first group of chemical formulation data.

A problem is to specify a more proper manufacturing process for a product as a material. A configuration of the present invention for solving the above problem is a manufacturing process optimization system 1 which includes an input device 12 which receives a final product and information on its manufacturing process, a central control device 11 which in accordance with a product management unit 21 stored in a main storage device 14, separates each process block constituting the manufacturing process into functions that the process thereof is responsible for, and selects the sensitivity of each separated function along the manufacturing process to thereby calculate process conditions in all manufacturing process, and an output device 13 which outputs the process conditions.

An information processing system according to an embodiment is configured to: acquire a numerical representation and a combination ratio for each of a plurality of component objects; calculate a first feature vector of each of the plurality of component objects by inputting a plurality of numerical representations corresponding to the plurality of component objects into a first machine learning model; calculate a second feature vector of each of the plurality of component objects by applying the combination ratio to the first feature vector for each of the plurality of component objects; and calculate a composite feature vector indicating features of a composite object obtained by combining the plurality of component objects, by inputting a plurality of second feature vectors corresponding to the plurality of component objects into a second machine learning model.

An information processing system according to an embodiment is configured to: acquire a numerical representation and a combination ratio for each of a plurality of component objects; calculate a first feature vector of each of the plurality of component objects by inputting a plurality of numerical representations corresponding to the plurality of component objects into a first machine learning model; calculate a second feature vector of each of the plurality of component objects by applying the combination ratio to the first feature vector for each of the plurality of component objects; and calculate a composite feature vector indicating features of a composite object obtained by combining the plurality of component objects, by inputting a plurality of second feature vectors corresponding to the plurality of component objects into a second machine learning model.

Examples of methods for predicting porosity are described herein. In some examples, a method includes predicting a height map. In some examples, the height map is of material for metal printing. In some examples, the method includes predicting a porosity of a precursor object. In some examples, predicting the porosity of the precursor object is based on the predicted height map.

An object of the present invention is to provide a material design system and a material design method, which each reduce computational complexity and enable efficient material design. A material design system according to the present invention, which designs a material that can achieve a desired material function, includes an input device receiving the desired material function, and an arithmetic unit calculating a constitutional material. The arithmetic unit includes a structure calculation part that calculates a molecular characteristic amount meeting the desired material function using a method that can express molecular characteristic amount exhibited by a set of two or more atoms or molecules, and a molecular calculation part that calculates the constituent material, which achieves the molecular characteristic amount calculated by the structure calculation part, using a method that can express a molecule itself.