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公开(公告)号：US20250069709A1
公开(公告)日：2025-02-27
申请号：US18943413
申请日：2024-11-11
Inventor： Jonathan Pradana MAILOA , Jiezhong Qiu , Shengyu Zhang
Abstract: This application relates to quantum chemistry. The method includes: obtaining training data for a molecular generative model; predicting, if a labeled property value in molecular property label data of a sample molecule in the training data for at least one of M properties is missing, a property value of the sample molecule for at least one property, to obtain molecular property prediction data of the sample molecule; obtaining molecular property tag data of the sample molecule based on the molecular property label data and the molecular property prediction data of the sample molecule; and training the molecular generative model based on the molecular property tag data of the sample molecule, to obtain a trained molecular generative model. This application supports training of the molecular generative model by using the training data without the labeled property value, molecular properties are more abundant and diversity of molecular data is improved.
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公开(公告)号：US20250061979A1
公开(公告)日：2025-02-20
申请号：US18722059
申请日：2022-12-22
Applicant: Kebotix Inc.
Inventor： Dennis Sheberla , Kevin Ryan
IPC: G16C20/70 , G06N3/0464 , G06N3/048 , G16C20/30
Abstract: The techniques described herein relate to computerized methods and apparatuses for predicting properties of molecules using a neural network. The neural network may include one or more layers to convert atom and bond features of an input molecule into respective atom and bond representations; a graph neural network configured to update the atom and bond representations; a molecule layer configured to convert the updated atom and bond representations into a molecule representation; and a target layer configured to predict one or more properties of the molecule based on the molecule representation. Prediction may include a regression operation to predict a single property value of the molecule, or a classification operation to predict probabilities of the molecule belonging to respective classes of a plurality of classes. The graph neural network may include a graph transformer network. The graph neural network may include a graph convolutional neural network.
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公开(公告)号：US20250054582A1
公开(公告)日：2025-02-13
申请号：US18687338
申请日：2022-08-25
Applicant: THE BOOTS COMPANY PLC
Inventor： Mark Johnson , Beverley Jane Elms , Paul James Tomlinson
Abstract: Computer implemented methods of choosing a solvent for a matrikine, apparatus and computer readable methods for implementing the computer implemented methods, methods of creating a matrikine composition, compositions (e.g., cosmetic compositions) made by the methods, for implementing the methods, and uses thereof are provided.
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公开(公告)号：US20250046400A1
公开(公告)日：2025-02-06
申请号：US18365581
申请日：2023-08-04
Applicant: Nissan North America, Inc. , United States of America as Represented by the Administrator of NASA
Inventor： Balachandran Gadaguntla Radhakrishnan , Shigemasa Kuwata , Masanobu Uchimura , Yasushi Ichikawa , William Curtis Tucker , Lauren J. Abbott , Ewa Papajak , Andrew Pablo Santos , Krishnan Swaminathan Gopalan , Justin B. Haskins
Abstract: A system can train a machine learning model to predict one or more properties of a molecule. The one or more properties may include a temperature of fusion and/or an entropy of fusion. The machine learning model can be trained based on a sample of molecules from a plurality of molecules. The system can apply the machine learning model to the plurality of molecules to predict the one or more properties for molecules of the plurality of molecules. The system can determine a plurality of candidate molecules from the plurality of molecules. The plurality of candidate molecules may be determined based on the one or more properties predicted for molecules of the plurality of molecules. The system can determine a target molecule of the plurality of candidate molecules to implement in a refrigeration system.
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公开(公告)号：US12203868B2
公开(公告)日：2025-01-21
申请号：US17477025
申请日：2021-09-16
Applicant: EVONIK OPERATIONS GMBH
Inventor： Philipp Isken , Sandra Bittorf , Oliver Kroehl , Claudia Bramlage , Markus Vogel , Stefan Silber , Gaetano Blanda , Olivia Lewis , Daniel Haake
IPC: G06K9/00 , B25J9/16 , G01N21/84 , G01N21/88 , G05B19/418 , G06F16/51 , G06N20/00 , G06T7/00 , G06T7/11 , G16C60/00 , G16C20/30 , G16C20/70
Abstract: A method for qualitative and/or quantitative characterization of a coating surface is provided, comprising: providing a program recognizing coating surface defect types; determining, by the program, whether a camera(s) coupled to the program is within a predefined distance range and/or within a predefined image acquisition angle range relative to a currently presented coating surface; depending on the determination: generating a feedback signal indicative of whether adjustment of the position of the camera(s) is within predefined distance range and/or within the predefined image acquisition angle range; and/or automatically adjusting the relative distance of the camera and and/or automatically adjusting the angle of the camera; enabling the camera to acquire an image of the coating surface only when the camera(s) is/are within the predefined distance range and/or image acquisition angle range; processing the digital image for recognizing coating surface defects; and outputting a characterization of the coating surface.
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Patent Agency Ranking
公开(公告)号：US20250022544A1
公开(公告)日：2025-01-16
申请号：US18714949
申请日：2022-11-09
Applicant: Konica Minolta, Inc.
Inventor： Yukihito NAKAZAWA , Michihiro OKUYAMA , Tomohiro OSHIYAMA
Abstract: An interaction impact evaluation method that enable highly accurate prediction of properties or a search for new substitute materials having desired properties is provided. According to the present invention, a step for selecting a kind of interaction due to a plurality of element materials, and a step for evaluating the degree to which the selected interaction is involved with the property of the composite material are performed to evaluate the impact of interaction with respect to the property of a composite material including a plurality of types of element materials.
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公开(公告)号：US20250006313A1
公开(公告)日：2025-01-02
申请号：US18700911
申请日：2022-10-13
Applicant: Invitae Corporation
Inventor： John Michael NICOLUDIS , Carlos L. ARAYA , Toby MANDERS , Alexandre COLAVIN , Gert KISS
Abstract: The present disclosure provides methods for automatically predicting the functional significance and clinical interpretation of variants (e.g., protein missense variants such as mutations) of unknown significance observed, e.g., in medical genetic testing, using the conformational dynamics of molecular structures (e.g., protein structures). The disclosure provides computer implemented methods, and integrated data, infrastructure, and software systems that can generate conformational dynamics (e.g., using molecular dynamics) of protein structures, compute features from these simulations, extract conformational states, initiate simulations for relevant variants (e.g., missense variants), and train, test, and deploy machine learning models for scoring the clinical significance of the variants.
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公开(公告)号：US20250003093A1
公开(公告)日：2025-01-02
申请号：US18293818
申请日：2022-08-03
Applicant: Texas Tech University System
Inventor： Chau-Chyun Chen , Yu-Jeng Lin
Abstract: A system and method for determining an activity coefficient (γi) for an electrolyte mixture by providing one or more processors, a. memory communicably coupled to the one or more processors and an output device communicably coupled to the one or more processors, calculating, using the one or more processors, the activity coefficient (γi) for the electrolyte mixture based, on association interactions between any species that associate, long-range interactions between ions, and short-range interactions between any species, providing the activity coefficient (γi) for the electrolyte mixture to the output device, and developing a chemical process or a product using the activity coefficient (γi) for the electrolyte mixture.
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公开(公告)号：US20240387005A1
公开(公告)日：2024-11-21
申请号：US18556092
申请日：2022-04-21
Applicant: Resonac Corporation
Inventor： Kyohei HANAOKA
Abstract: An input data generation system is an input data generation system generating input data for machine learning for predicting the properties of a material based on a raw material having a known structure, and includes at least one processor, in which at least one processor acquires partial structure data indicating a partial structure from a database, receives at least the input of raw material structure data for specifying the structure of the raw material and blending ratio data indicating a ratio of the blending of the raw material, generates partial structure input data indicating the partial structure existing in the structure of the raw material, on the basis of the partial structure data and the raw material structure data, generates input data by reflecting the blending ratio data on the partial structure input data of the raw material, and inputs the input data to a machine learning model.
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公开(公告)号：US12148138B2
公开(公告)日：2024-11-19
申请号：US17033779
申请日：2020-09-26
Applicant: Carl Zeiss X-ray Microscopy, Inc.
Inventor： Matthew Andrew
Abstract: Multivariant feature extraction is used for training volumes or 2D images, (real or synthetic) coupled to process (effective) values probably obtained from direct simulation. These features are coupled with machine learning/regression algorithms to make a predictive model for the effective property. This model can then be used on a real geometry of a sample for effective parameter prediction.
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