Samsung Electronics Co., Ltd.

Samsung Sdi Co., Inc.

Sungkyunkwan University

Younsuk Choi

Youngchun Kwon

Jiho Yoo

Dongseon Lee

Kyungdoc Kim

Youngmin Nam

Won Seok Oh

Hyun Do Choi

Hyunjeong Jeon

Wooram Hong

Gahee Kim

Seokho Kang

Hyukju Kwon

Jin Woo Kim

Sangyoon Lee

Jeonghun Kim

Soonok Jeon

Youngjin Park

Yongsik Jung

Jun-Won Jang

Jaejun Chang

Joon-Kee Cho

Byung-Kwon Choi

Taesin Ha

Min Sik Park

Bosung Kim

Hyosug Lee

Won Je Choi

Dal Heo

Hyungtae Seo

Jaikwang Shin

Kwang Hee Lee

Jinwoo Park

Dong-Seok Leem

Kyuyoung Hwang

Hyeonho Choi

Seungyeon Kwak

Sangho Park

Juhyun Kim

Sangmo Kim

Kum Hee Lee

Wook Hwan Kim

Yeonsook Chung

Aram Jeon

Jhunmo Son

Hosuk Kang

Eunsuk Kwon

Whail Choi

Ohkyu Kwon

Hyesung Choi

Hyejin Bae

Hasup Lee

Sunghan Kim

Bum Woo Park

Minsik Min

Hwang Suk Kim

Masaki Numata

Wonseok Oh

Changho Noh

Hiroshi Miyazaki

Minsik Park

Jeong-Ju Cho

Won-joon Son

Won-Joon Son

Tae-Rae Kim

Hyun Sik Chae

Misuk Kim

In Koo Kim

Kyung Doc Kim

Sae Youn Lee

Minsu Chang

The present disclosure provides optical monitoring devices. In some embodiments, the optical monitoring device includes a heating stirrer, a floating barrel, and a support body arranged to support the floating barrel. The floating barrel includes a barrel body facing the heating stirrer and spaced apart from the heating stirrer by a gap, a cavity provided in the barrel body and configured to accommodate at least one of a vessel and a conduit, an irradiator arranged in the barrel body and configured to radiate light to the cavity, and a light receiver arranged in the barrel body, aligned with the irradiator and the cavity, and configured to receive light from the cavity.

Optical monitoring device

An organometallic compound represented by Formula 1:M(L)(L)  Formula 1

Organometallic compound, organic light-emitting device including the same, and diagnostic composition including the organometallic compound

Provided are a condensed cyclic compound, an organic light-emitting device including the condensed cyclic compound, and an electronic device including the organic light-emitting device. The condensed cyclic compound is represented by Formula 1, wherein Ris a group represented by Formula 2-1 and Ris a group represented by Formula 2-2.

Condensed cyclic compound, organic light-emitting device including same, and electronic device including organic light-emitting device

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.

Sealing structure and material containing device including the same

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.

Heat exchanger and heat exchanging system comprising the same

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, Arand Ar, Z, L, L, and Rto Rare the same as defined in the detailed description.

Compound and film and photoelectric diode and organic sensor and electronic device

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.

Method and apparatus for generating chemical structure

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.

Apparatus and method of optimizing experimental conditions using neural network

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.

Method and apparatus for generating chemical structure using neural network

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.

Method and apparatus for generating a chemical structure using a neural network

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.

Apparatus and method for generating molecular structure

An organometallic compound represented by Formula 1:M(L)(L)(L)&#x2003;&#x2003;Formula 1

Organometallic compound and organic light-emitting device including the same

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.

Method and device for searching new material

A condensed cyclic compound represented by Formula 1:Ar-(L)-B-(L)-Ar&#x2003;&#x2003;Formula 1

Bipyridine derivatives and their uses for organic light emitting diodes

A structure-generating method for generating a structure candidate of a new material including: by a structure-generating processor: performing machine learning on a machine learning model, wherein the machine learning model is configured to provide a result based on a descriptor of a material, a physical property of the material, and a structure of the material; and generating a structure candidate of the new material based on the result of the machine learning, wherein the new material has a target physical property, and wherein the descriptor of the material, the physical property of the material, and the structure of the material are stored in a database.

Method and apparatus for new material discovery using machine learning on targeted physical property

Growing community of inventors

Seongnam-si, South Korea

Younsuk Choi