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1.发明公开
公开(公告)号：EP4195089A1
公开(公告)日：2023-06-14
申请号：EP22202886.2
申请日：2022-10-21
发明人： PILLAI, PRAJITH , CHAUDHURI, ANIRBAN , PAL, PARAMA , RAI, BEENA
摘要： Present application provides systems and method implement apply a Physics-Informed Neural Network (PINN) for inversely calculating the effective material parameters of a multi-dimensional metamaterial from its scattered field(s). By employing a loss function based on the Helmholtz wave equation, performance of a metamaterial is modeled by the system the dependance of resonant behavior on the homogenized electric permittivity distribution profile generated by the PINN is demonstrated.
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2.发明公开
公开(公告)号：EP4102508A3
公开(公告)日：2022-12-21
申请号：EP22177824.4
申请日：2022-06-08
摘要： Traditionally, new alloy development and processing involved various high-end expansive experiments, huge development time and cost of required man-hours. One of the major issues, which limits the ability for materials scientists to design metallic materials from atoms using Molecular Dynamics (MD), is the lack of accurate interatomic molecular dynamics potentials (MDPs). Suitable MDPs of desired alloy systems enable new alloy compositions and related properties, but however, this is very difficult and time-consuming process. The present disclosure enables developing molecular dynamics potential for new/traditional metallic alloys for their simulated structural, thermodynamic, and mechanical property predictions. Present disclosure provides systems and methods for generating MDP for multi-element alloy systems wherein both Body Centered Cubic (BCC) element type and/or a Face Centered Cubic (FCC) element type are combined. Pure elements and multi-element alloys of combinations of BCC and FCC elements are modeled for predicting their various structural, thermodynamic, and mechanical properties.
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3.发明公开
公开(公告)号：EP4336163A1
公开(公告)日：2024-03-13
申请号：EP23195768.9
申请日：2023-09-06
发明人： MAITI, SOUMYADIPTA , RAI, BEENA , KAUSLEY, SHANKAR BALAJIRAO , SAINI, PARVESH , BHATTACHARJEE, SURYADIP , DHRANGDHARIYA, PRIYANKKUMAR DHIRAJLAL
IPC分类号： G01M5/00 , G06F30/23 , G06F119/04 , G06F111/08 , G06F113/26
摘要： Most techniques to estimate the service life of coatings are experimental in nature, cost expensive and are computationally heavy. Present disclosure provides systems and methods that predict the combined effects of crack path propagation and zones of delamination, that form on coating material and its surface due to weathering. The system of the present disclosure implemented a combined Finite Element Method (FEM) and Monte Carlo based simulation approach to capture the effects of delamination and crack propagation, respectively (202, 204, 206, 208). The crack paths are predicted using a probabilistic model, considering crack propagation, branching, and keeping a record of crack age. Stress distribution computations are performed using FEM to understand stress concentration zones and delamination behavior with time, which is methodically also combined with the time sequence of cracking as well.
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公开(公告)号：EP4239641A1
公开(公告)日：2023-09-06
申请号：EP23153699.6
申请日：2023-01-27
发明人： KUMAR, DHARMENDR , RAI, BEENA , AGARWAL, ABHISHEK , JAIN, VINAY
IPC分类号： G16C20/64
摘要： This disclosure relates generally to methods and systems for screening and design of corrosion inhibitors. Conventional techniques in the art for predicting the inhibition efficiency of the molecule are not so accurate, where the inhibition efficiency is one of the important parameter for screening and design of corrosion inhibitors. In the present disclosure, the IE prediction model is developed by training a multi-task network model using the molecular descriptors of different classes of the molecules, and also the experimental conditions of the molecule. Further, a unique screening technique is disclosed that screens the molecules using various parameters including the inhibition efficiency of the molecule, an energy gap of the molecule, a synergistic effect of the molecules and an interaction energy of the molecules. Further the present disclosure allows to design a new molecule by making changes to the molecules that suits based on the end applications.
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公开(公告)号：EP4002207A1
公开(公告)日：2022-05-25
申请号：EP21205782.2
申请日：2021-11-01
发明人： DUTTA, JAYITA , DESHPANDE, PARIJAT , PATWARDHAN, MANASI SAMARTH , KARANDE, SHIRISH SUBHASH , KAUSLEY, SHANKAR , KEDIA, PRIYA , KAPSE, SHRIKANT ARJUNRAO , RAI, BEENA
摘要： Traditional food quality monitoring systems fail to monitor the variation of food quality in real-time scenarios. Existing machine learning approaches require dedicated data models for different classes of food items due to differences in characteristics of different food items,. Also, to generate such data models, a lot of annotated data is required per food item, which are expensive. The disclosure herein generally relates to monitoring and shelf-life prediction of food items, and, more particularly, to system and method for real-time monitoring and shelf-life prediction of food items. The system generates a data model using a knowledge graph indicative of a hierarchical taxonomy for a plurality of categories of the plurality of food items, which in turn contains metadata representing similarities in physio-chemical degradation pattern of different classes of the food items. This data model serves as a generic data model for real-time shelf-life prediction of different food items.
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公开(公告)号：EP4343651A1
公开(公告)日：2024-03-27
申请号：EP23192752.6
申请日：2023-08-22
发明人： KEDIA, PRIYA , KAUSLEY, SHANKAR , PATWARDHAN, MANASI SAMARTH , KARANDE, SHIRISH SUBHASH , RAI, BEENA , DUTTA, JAYITA , DESHPANDE, PARIJAT , SRIRAMAN, ANAND , KAPSE, SHRIKANT ARJUNRAO
IPC分类号： G06Q10/087 , G06Q10/04
摘要： This disclosure relates generally to method and system for predicting shelf life of perishable food items. In supply chain management, current technology provides limited capability in providing relation between visual image of food item and a quality parameter value at different storage conditions. The system includes a quality parameter prediction module and a shelf life prediction module. The method obtains input data from user comprising a visual data and a storage data of each food item. The quality parameter prediction module determines a current quality parameter value of the food item from a look-up table. The shelf life prediction module predicts the shelf life of food item based on the current quality parameter value, a critical quality parameter value, and the storage data. The look-up table comprising a plurality of weather zones are generated based on relationship dynamics between the visual image of food item and the quality parameter value.
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7.发明公开
公开(公告)号：EP4290525A1
公开(公告)日：2023-12-13
申请号：EP23177316.9
申请日：2023-06-05
IPC分类号： G16C10/00
摘要： This disclosure relates generally to a method to determine an optimal set of atom centered symmetry functions. One or more parameters associated with one or more atom centered symmetry functions (ACSFs) are received. An initial set of ACSFs is generated by varying the one or more parameters. A histogram with a prespecified bin size is constructed to obtain a distribution of value of each of the initial set of ACSFs. A pruned list of ACSFs is obtained based on width and maximum value of the distribution of the value of initial set of ACSFs. The pruned list of ACSFs is sorted in decreasing order of spread to obtain a sorted list of ACSFs. An optimal set of one or more shortlisted ACSFs is determined by traversing through the sorted list of ACSFs. A high dimensional neural network potential is trained based on the optimal set of one or more shortlisted ACSFs.
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8.发明公开
公开(公告)号：EP4170322A1
公开(公告)日：2023-04-26
申请号：EP22197713.5
申请日：2022-09-26
发明人： PARAPPILLIL BABY, RENJU , PAL, PARAMA , RAI, BEENA
IPC分类号： G01N21/47
摘要： This disclosure relates to portable speckle imaging system and method for automated speckle activity map based dynamic speckle analysis. The embodiments of present disclosure herein address unresolved problem of capturing variations in speckle patterns where noise is completely removed and dependency on intensity of variations in speckle patterns is eliminated. The method of the present disclosure provides a correlation methodology for analyzing laser speckle images for applications such as seed viability, fungus detection, surface roughness analysis, and/or the like by capturing temporal variation from frame to frame and ignoring the intensity of speckle data after denoising, thereby providing an effective mechanism to study speckle time series data. The system and method of the present disclosure performs well in terms of time efficiency and visual cues and requires minimal human intervention.
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9.发明公开
公开(公告)号：EP4401083A3
公开(公告)日：2024-07-24
申请号：EP24150004.0
申请日：2024-01-02
IPC分类号： G16C60/00
摘要： Conventional approaches are not efficient for the screening of active multi component alloy catalysts. Present disclosure provides systems and method that incorporate elements of materials chemistry, artificial intelligence (AI)/machine learning (ML) and optimization for the screening of such active catalysts for a target chemical reaction. Given a set of elements constituting the alloy and the target reaction, surface atomic structures of several different alloy composition and configurations are constructed. Then, adsorption energy of key reaction intermediates on the surface atomic structures are computed using quantum chemical methods (QCM). Next, an AI/ML model is trained using data obtained from QCM to predict the adsorption energy of the reaction intermediates on the alloy surface. Finally, numerical optimization is used to identify alloy compositions that maximize the activity and selectivity of the catalyst for a target chemical reaction.
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10.发明公开
公开(公告)号：EP4401083A2
公开(公告)日：2024-07-17
申请号：EP24150004.0
申请日：2024-01-02
IPC分类号： G16C60/00
摘要： Conventional approaches are not efficient for the screening of active multi component alloy catalysts. Present disclosure provides systems and method that incorporate elements of materials chemistry, artificial intelligence (AI)/machine learning (ML) and optimization for the screening of such active catalysts for a target chemical reaction. Given a set of elements constituting the alloy and the target reaction, surface atomic structures of several different alloy composition and configurations are constructed. Then, adsorption energy of key reaction intermediates on the surface atomic structures are computed using quantum chemical methods (QCM). Next, an AI/ML model is trained using data obtained from QCM to predict the adsorption energy of the reaction intermediates on the alloy surface. Finally, numerical optimization is used to identify alloy compositions that maximize the activity and selectivity of the catalyst for a target chemical reaction.
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