Abstract: Disclosed are a compound represented by Chemical Formula 1, a film, a photoelectric diode, an organic sensor, and an electronic device. In Chemical Formula 1, Ar1 and Ar2, Z, L1, L2, and R1 to R6 are the same as defined in the detailed description.

Abstract: A method and apparatus for generating a new chemical structure using a chemical structure generation model are provided. The method includes receiving a first descriptor of a first chemical structure, encoding the first descriptor into a latent variable using an encoder of the chemical structure generation model, generating a second descriptor by decoding the latent variable using a decoder of the chemical structure generation model, generating the second chemical structure corresponding to the second descriptor.

Abstract: A method of predicting a synthetic path may include: receiving a target molecule descriptor corresponding to a target material, predicting one or more synthetic path descriptor candidates representing multi-step synthetic paths corresponding to the target molecule descriptor using a neural network-based predictive model, the neural network-based predictive model being trained based on a synthetic path descriptor in a form of a character string obtained by converting multi-step synthetic path data; and outputting the one or more synthetic path descriptor candidates.

Abstract: Generating a new chemical structure by using a neural network using an expression region that expresses a particular property in a descriptor or an image for a reference chemical structure. The new chemical structure may be generated by changing a partial structure in the reference chemical structure that corresponds to the expression region.

Abstract: An apparatus for optimizing experimental conditions by using a neural network may calculate a prediction yield and accuracy of the prediction yield by using a neural network-based experimental prediction model. The apparatus may optimize the experimental conditions by determining an experiment priority of a respective experiment condition combination based on the prediction yield and the prediction accuracy and receiving a feedback of results of experiments performed according to the experiment priority.

Abstract: A heat exchanger is provided. The heat exchanger includes a target area that is a target for heat exchange; and a flow path structure. The flow path structure includes at least one inlet; at least one outlet; a first flow path connected to each of the at least one inlet and the at least one outlet, and extending along a first side of the target area; and a second flow path connected to each of the at least one inlet and the at least one outlet, and extending along a second side, different from the first side, of the target area.

Abstract: Generating a new chemical structure by using a neural network using an expression region that expresses a particular property in a descriptor or an image for a reference chemical structure. The new chemical structure may be generated by changing a partial structure in the reference chemical structure that corresponds to the expression region.

Abstract: A sealing structure may include a lid including a first lid face, a second lid face opposite to the first lid face, and a fragile area between the first lid face and the second lid face, a cover including a first cover face facing the second lid face and covering the fragile area and a second cover face opposite to the first cover face, wherein a first distance between the second lid face and the first cover face is substantially equal to or less than a second distance between the first cover face and the second cover face, and a connector configured to connect the lid and the cover.

Abstract: A method of optimizing synthetic conditions includes receiving a graph-type descriptor comprising at least one of structural information of at least one reactant and structural information of a target product to be synthesized by the reactant; determining combinations of synthetic conditions for generating the target product by applying the graph-type descriptor to a prediction neural network model; selecting at least one initial condition combination from among the combinations based on a first confidence corresponding to a yield of the combinations; updating the prediction neural network model based on a ground-truth yield obtained from a result of an experiment with the initial condition combination; determining a priority of the combinations based on the updated prediction neural network model; and determining subsequent combinations of synthetic conditions based on the determined priority.

Abstract: A method of generating a chemical structure performed by a neural network device includes receiving a target property value and a target structure characteristic value; selecting first generation descriptors; generating second generation descriptors; determining, using a first neural network of the neural network device, property values of the second generation descriptors; determining, using a second neural network of the neural network device, structure characteristic values of the second generation descriptors; selecting, from the second generation descriptors, candidate descriptors that satisfy the target property value and the target structure characteristic value; and generating, using the second neural network of the neural network device, chemical structures for the selected candidate descriptors.

Abstract: A material synthesis apparatus includes: at least one device configured to synthesize a material; a user interface configured to obtain information on a target product; and a processor, wherein the processor is configured to: determine synthesis conditions for preparing the target product using a pretrained synthesis prediction model; calculate a first synthesis method for preparing the target product based on the synthesis conditions; and control the at least one device based on the first synthesis method.

Abstract: A method of generating a molecular structure includes generating, based on a predetermined number of a plurality of nodes, all possible two-dimensional (2D) graphs and a plurality of edges representing connections between the plurality of nodes and, for each 2D graph from among the generated 2D graphs, generating all possible molecular structures based on the 2D graph by substituting each of the plurality of nodes with a polygonal ring structure including carbon atoms and substituting the edges with bonds between polygonal ring structures. Also, a method includes substituting at least one of carbon atoms included in the polygonal ring structures with an atom other than a carbon atom.

Abstract: An organometallic compound represented by Formula 1: M(L1)n1(L2)n2(L3)n3??Formula 1 wherein in Formula 1, M, L1, L2, L3, n1, n2, and n3 are the same as defined in the specification.

Abstract: An apparatus for generating a target product by using a neural network is configured to: predict candidate reactant combinations for generating the target product by using a pre-trained retrosynthesis prediction model; predict a prediction product with respect to each of the candidate reactant combinations by using a pre-trained reaction prediction model; and determine an experimental priority order of the candidate reactant combinations based on a result of comparing the target product with the prediction product.

Abstract: A method for searching a new material includes: performing a learning on a material model, which is modeled based on a known material; determining a candidate material by inputting a targeted physical property to a result of the learning; and determining the new material from the candidate material.

Abstract: A neural network apparatus for generating a new chemical structure may receive a structure input of a chemical structure; generate, based on the structure input, a negative attention vector that indicates a respective probability of presence of each of a plurality of blacklists in the structure input; generate a structure expression by encoding the structure input; generate a final reverse blacklist vector that does not include the plurality of blacklists, based on the negative attention vector and the structure expression; and generate the new chemical structure by decoding the final reverse blacklist vector.

Abstract: A method for searching a new material includes: performing a learning on a material model, which is modeled based on a known material; determining a candidate material by inputting a targeted physical property to a result of the learning; and determining the new material from the candidate material.

Abstract: Disclosed are a compound represented by Chemical Formula 1, a film, a photoelectric diode, an organic sensor, and an electronic device. In Chemical Formula 1, Ar1 and Ar2, Z, L1, L2, and R1 to R6 are the same as defined in the detailed description.

Abstract: An apparatus for optimizing experimental conditions by using a neural network may calculate a prediction yield and accuracy of the prediction yield by using a neural network-based experimental prediction model. The apparatus may optimize the experimental conditions by determining an experiment priority of a respective experiment condition combination based on the prediction yield and the prediction accuracy and receiving a feedback of results of experiments performed according to the experiment priority.

Abstract: A method and apparatus for generating a new chemical structure using a chemical structure generation model are provided. The method includes receiving a first descriptor of a first chemical structure, encoding the first descriptor into a latent variable using an encoder of the chemical structure generation model, generating a second descriptor by decoding the latent variable using a decoder of the chemical structure generation model, generating the second chemical structure corresponding to the second descriptor.