Techniques to suggest a set of source ingredients that can be used to recreate functional properties of a target food item, using artificial intelligence, are disclosed. A computer model determines, for the target food item, an ingredient quantities vector and an ingredient inclusion vector based on a matrix of chemical compound source ingredient vectors of all source ingredients. The ingredient inclusion vector indicates which source ingredients from a plurality of source ingredients to include in the ingredient set, and the ingredient quantities vector indicates the quantity or amount of each source ingredient such that a corresponding volatile profile of the ingredient set is similar to that of the target food item. A volatile profile for the ingredient set, which is determined from the matrix of chemical compound source ingredient vectors, the ingredient inclusion vector, and the ingredient quantities vector, mimics the target food item's volatile profile.

Techniques to suggest a set of source ingredients that can be used to recreate functional properties of a target food item, using artificial intelligence, are disclosed. A computer model determines, for the target food item, an ingredient quantities vector and an ingredient inclusion vector based on a matrix of chemical compound source ingredient vectors of all source ingredients. The ingredient inclusion vector indicates which source ingredients from a plurality of source ingredients to include in the ingredient set, and the ingredient quantities vector indicates the quantity or amount of each source ingredient such that a corresponding volatile profile of the ingredient set is similar to that of the target food item. A volatile profile for the ingredient set, which is determined from the matrix of chemical compound source ingredient vectors, the ingredient inclusion vector, and the ingredient quantities vector, mimics the target food item's volatile profile.

Techniques to suggest a set of source ingredients that can be used to recreate functional properties of a target food item, using artificial intelligence, are disclosed. A computer model determines, for the target food item, an ingredient quantities vector and an ingredient inclusion vector based on a matrix of chemical compound source ingredient vectors of all source ingredients. The ingredient inclusion vector indicates which source ingredients from a plurality of source ingredients to include in the ingredient set, and the ingredient quantities vector indicates the quantity or amount of each source ingredient such that a corresponding volatile profile of the ingredient set is similar to that of the target food item. A volatile profile for the ingredient set, which is determined from the matrix of chemical compound source ingredient vectors, the ingredient inclusion vector, and the ingredient quantities vector, mimics the target food item's volatile profile.

Techniques to suggest a set of source ingredients that can be used to recreate functional properties of a target food item, using artificial intelligence, are disclosed. A computer model determines, for the target food item, an ingredient quantities vector and an ingredient inclusion vector based on a matrix of chemical compound source ingredient vectors of all source ingredients. The ingredient inclusion vector indicates which source ingredients from a plurality of source ingredients to include in the ingredient set, and the ingredient quantities vector indicates the quantity or amount of each source ingredient such that a corresponding volatile profile of the ingredient set is similar to that of the target food item. A volatile profile for the ingredient set, which is determined from the matrix of chemical compound source ingredient vectors, the ingredient inclusion vector, and the ingredient quantities vector, mimics the target food item's volatile profile.

A system can include one or more processors configured to access at least one parameter of a material, generate a plurality of structures of the material using the at least one parameter, determine a state of each structure of the plurality of structures using the at least one parameter, determine a difference between the state of each structure of the plurality of structures and a ground state value, evaluate a convergence condition responsive to determining the difference between the state of each structure of the plurality of structures and the ground state value, and output at least one structure of the plurality of structures responsive to the convergence condition being satisfied.

A system can include one or more processors configured to access at least one parameter of a material, generate a plurality of structures of the material using the at least one parameter, determine a state of each structure of the plurality of structures using the at least one parameter, determine a difference between the state of each structure of the plurality of structures and a ground state value, evaluate a convergence condition responsive to determining the difference between the state of each structure of the plurality of structures and the ground state value, and output at least one structure of the plurality of structures responsive to the convergence condition being satisfied.

Volatile organic compounds classification by receiving test data associated with detecting volatile organic compounds (VOCs), analyzing the test data according to a set of data features associated with known VOCs, determining a match between each feature of the test data and a corresponding feature of the set of data features, yielding a set of matches, defining a first degree of anomaly for the test data according to the set of matches, and classifying the test data according to the first degree of anomaly.

Presented are new, earth-abundant lithium superionic conductors, Li.sub.3Y(PS.sub.4).sub.2 and Li.sub.5PS.sub.4Cl.sub.2, that emerged from a comprehensive screening of the Li—P—S and Li-M-P—S chemical spaces. Both candidates are derived from the relatively unexplored quaternary silver thiophosphates. One key enabler of this discovery is the development of a first-of-its-kind high-throughput first principles screening approach that can exclude candidates unlikely to satisfy the stringent Li.sup.+ conductivity requirements using a minimum of computational resources. Both candidates are predicted to be synthesizable, and are electronically insulating. Systems and methods according to present principles enable new, all-solid-state rechargeable lithium-ion batteries.

Presented are new, earth-abundant lithium superionic conductors, Li.sub.3Y(PS.sub.4).sub.2 and Li.sub.5PS.sub.4Cl.sub.2, that emerged from a comprehensive screening of the Li—P—S and Li-M-P—S chemical spaces. Both candidates are derived from the relatively unexplored quaternary silver thiophosphates. One key enabler of this discovery is the development of a first-of-its-kind high-throughput first principles screening approach that can exclude candidates unlikely to satisfy the stringent Li.sup.+ conductivity requirements using a minimum of computational resources. Both candidates are predicted to be synthesizable, and are electronically insulating. Systems and methods according to present principles enable new, all-solid-state rechargeable lithium-ion batteries.

A soldering process method includes the following steps. A temperature profile of generating a solder structure is measured. A final product of the solder structure is tested and recorded. A machine learning method is used to repeatedly compare and analyze a relationship between a plurality of the temperature profiles of the solder structure and a corresponding final product of the solder structure so as to find an optimal temperature profile model in accordance with quality control requirements.