This disclosure relates generally to a system and method for monitoring and quality evaluation of perishable food items in quantitative terms. Current technology provides limited capability for controlling environmental conditions surrounding the food items in real-time or any quantitative measurement for the degree of freshness of the perishable food items. The disclosed systems and methods facilitate in quantitative determination of freshness of food items by utilizing sensor data and visual data obtained by monitoring the food item. In an embodiment, the system utilizes a pre-trained CNN model and a RNN model, where the pertained CNN model is further fine-tined while training the RNN model to provide robust quality monitoring of the food items. In another embodiment, a rate kinetic based model is utilized for determining reaction rate order of the food item at a particular post-harvest stage of the food item so as to determine the remaining shelf life thereof.

A system and method for feedback-driven automated drug discovery which combines machine learning algorithms with automated research facilities and equipment to make the process of drug discovery more data driven and less reliant on intuitive decision-making by experts. In an embodiment, the system comprises automated research equipment configured to perform automated assays of chemical compounds, a data platform comprising drug databases and an analysis engine, a bioactivity and de novo modules operating on the data platform, and a retrosynthesis system operating on the drug discovery platform, all configured in a feedback loop that drives drug discovery by using the outcome of assays performed on the automated research equipment to feed the bioactivity module and retrosynthesis systems, which identify new molecules for testing by the automated research equipment.

A synthesis machine for preparation of a targeted inorganic material for recommended synthesis by a computer program that determines optimal solid-state methods for synthesis of an inorganic material. The computational method involves inputting a target inorganic material, querying structural data and thermodynamic data for the target inorganic material, enumerating possible synthetic reactions to construct a synthetic reaction database with a viable subset of the possible synthetic methods. The routine generates a nucleation metric and competition metric that are combined to provide recommended synthetic methods. The output for each of the recommended syntheses are input into a robotic synthesis machine where the delivery of reactants, reaction conditions, and analysis of extent of reaction, and product quality is controlled by a processor.

A synthesis machine for preparation of a targeted inorganic material for recommended synthesis by a computer program that determines optimal solid-state methods for synthesis of an inorganic material. The computational method involves inputting a target inorganic material, querying structural data and thermodynamic data for the target inorganic material, enumerating possible synthetic reactions to construct a synthetic reaction database with a viable subset of the possible synthetic methods. The routine generates a nucleation metric and competition metric that are combined to provide recommended synthetic methods. The output for each of the recommended syntheses are input into a robotic synthesis machine where the delivery of reactants, reaction conditions, and analysis of extent of reaction, and product quality is controlled by a processor.

A training method for a neural network includes determining first disassembly paths of a plurality of first molecules, and obtaining a first cost dictionary based on the first disassembly paths of the first molecules. The method also includes determining molecular expression information of second molecules based on the first disassembly paths of the first molecules, and determining a plurality of third molecules from the second molecules, each of the third molecules representing a class of the second molecules. The method further includes obtaining a second cost dictionary based on second disassembly paths of the third molecules, and performing training based on the first cost dictionary and the second cost dictionary to obtain a target neural network. The target neural network being configured to output cost value information corresponding to a target molecule according to input molecular expression information of the target molecule.

A training method for a neural network includes determining first disassembly paths of a plurality of first molecules, and obtaining a first cost dictionary based on the first disassembly paths of the first molecules. The method also includes determining molecular expression information of second molecules based on the first disassembly paths of the first molecules, and determining a plurality of third molecules from the second molecules, each of the third molecules representing a class of the second molecules. The method further includes obtaining a second cost dictionary based on second disassembly paths of the third molecules, and performing training based on the first cost dictionary and the second cost dictionary to obtain a target neural network. The target neural network being configured to output cost value information corresponding to a target molecule according to input molecular expression information of the target molecule.

A method for synthesizing methanol from a raw material using a set of process parameters, wherein a mathematical model is provided that is configured for calculating, based on the process parameters and on at least one material property of the raw material, an expected amount of at least one by-product of the synthesizing of the methanol, wherein the following steps are performed at least once in the stated order, applying the mathematical model to current values for the process parameters, and changing a respective set point for at least one of the process parameters such that based on the mathematical model a lower amount of the by-products is expected under condition that at least a predetermined amount of the methanol can be synthesized,

A method for synthesizing methanol from a raw material using a set of process parameters, wherein a mathematical model is provided that is configured for calculating, based on the process parameters and on at least one material property of the raw material, an expected amount of at least one by-product of the synthesizing of the methanol, wherein the following steps are performed at least once in the stated order, applying the mathematical model to current values for the process parameters, and changing a respective set point for at least one of the process parameters such that based on the mathematical model a lower amount of the by-products is expected under condition that at least a predetermined amount of the methanol can be synthesized,

A material synthesis apparatus may include a synthesis device configured to perform a synthesis of a material of a target product; a communication interface configured to receive a first synthesis method of the target product, the first synthesis method being calculated by an external apparatus using a previously trained synthesis prediction model; and a processor configured to: determine first commands for synthesizing the target product based on the first synthesis method, schedule an order in which the first commands are executed, and control the synthesis device based on the scheduled order.

An analysis method of crystal growth conditions includes a step of calculating an evaluation function on the basis of results obtained by measuring crystals grown under varied crystal growth conditions, a step of performing machine learning of the evaluation function, and a step of obtaining optimum crystal growth conditions from a result of the machine learning, wherein the evaluation function is based on a difference between crystal quality data of an ideal crystal and crystal quality data of the crystal having been grown.