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.

Simulation system for selecting an alloy and a production process for a workpiece to be produced having amorphous properties, wherein the system includes : an input unit, for inputting a requirements profile for the workpiece to be produced, at least one memory unit, to store information data, wherein the information data specifies information concerning physical and/or chemical and/or mechanical properties of a number of alloys for manufacturing workpieces having amorphous properties and information concerning production processes, an analysis unit, to simulate a number of workpieces according to the requirements profile and the information data to create simulation data, to assess the simulated workpieces on the basis of the simulation data and the requirements profile, to select an alloy and a production process for the workpiece to be produced from assessment, and an output unit, to output the selected alloy and the selected production process.

A system for predicting a one-sided property value for one or more material candidates includes a processor and a memory communicably coupled to the processor. The memory includes a stored acquisition module and a machine learning (ML) module. The acquisition module is configured to select a training data set with a given material property. The training data set includes a first subset of materials having the material property within a predefined range and a second subset of materials having the material property outside the predefined range. Also, the ML module is configured to impute a fixed value for the material property outside the predefined range and train a ML model to predict the material property using imputed fixed value.

A system for predicting a one-sided property value for one or more material candidates includes a processor and a memory communicably coupled to the processor. The memory includes a stored acquisition module and a machine learning (ML) module. The acquisition module is configured to select a training data set with a given material property. The training data set includes a first subset of materials having the material property within a predefined range and a second subset of materials having the material property outside the predefined range. Also, the ML module is configured to impute a fixed value for the material property outside the predefined range and train a ML model to predict the material property using imputed fixed value.

Aspects of the present disclosure relate to computing systems and computational methods for docking a library of compounds against a massive amount of conformations of a protein of interest.

Aspects of the present disclosure relate to computing systems and computational methods for docking a library of compounds against a massive amount of conformations of a protein of interest.

A storage medium storing a program that causes a computer to execute a process that includes acquiring a first model that is trained based on training data which indicates a first combination of constituent values of a target object and an environmental value in an experiment on the target object with associating with a characteristic value and that specifies a mean value and a deviation value of the characteristic value; acquiring a second model that is trained based on training data which indicates a second combination of the constituent values and an allowable condition for the experiment, and that specifies the allowable condition; and generating a solution set for the first combination by performing multi-objective optimization by a penalty term based on the allowable condition, a first objective function, and a second objective function.

Metallic alloy development has been traditionally based on experimental or theoretical equilibrium phase diagrams and the like. The synthesis, processing and mechanical testing of small and large real samples are a challenging task requiring huge amount of effort in terms of time, money, resource, tedious testing and processing equipment and man-hour for which conventional Calphad calculations etc. alone do not help much in their local structure and related property prediction. Embodiments of the present disclosure provide simulation systems and methods for structure evolution and property prediction Molecular Dynamics (MD) combined with accelerated Monte Carlo techniques, wherein information on atomic elements and composition specific to alloy material is obtained to generate a MD potential file that is further used to generate a 3D structure file by executing a structure equilibration technique. An optimized evolved 3D structure file is then generated that has atomic positions output and/or thermodynamic output for predicting properties.

Metallic alloy development has been traditionally based on experimental or theoretical equilibrium phase diagrams and the like. The synthesis, processing and mechanical testing of small and large real samples are a challenging task requiring huge amount of effort in terms of time, money, resource, tedious testing and processing equipment and man-hour for which conventional Calphad calculations etc. alone do not help much in their local structure and related property prediction. Embodiments of the present disclosure provide simulation systems and methods for structure evolution and property prediction Molecular Dynamics (MD) combined with accelerated Monte Carlo techniques, wherein information on atomic elements and composition specific to alloy material is obtained to generate a MD potential file that is further used to generate a 3D structure file by executing a structure equilibration technique. An optimized evolved 3D structure file is then generated that has atomic positions output and/or thermodynamic output for predicting properties.

Reactivity mapping methods are provided. A method may include: analyzing each of a group of inorganic particles to generate data about physical and/or chemical properties of the inorganic particles; and generating correlations between the properties of inorganic particles based on the data.