Explainable Predictions of Multi-Component Oxides Enabled by Attention-Based Neural Networks
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Multicomponent oxides (MCOs) have attracted considerable attention due to their wide range of applications. However, the extensive search space of MCO components and the scarcity of MCO crystal structures in existing literature have promoted the use of deep machine learning methods for predicting MCO properties. Despite these advances, accurately predicting the thermal expansion of MCOs across wide temperature and composition ranges remains a complex task. An innovative attention-based deep learning model was introduced in this study. The proposed two self-attention modules have greatly improved the performance of this model, achieve a 86.88% improvement in root mean square error for thermal expansion predictions of MCOs. Additionally, the model demonstrates impressive adaptability and interpretability. Its training results can further aid in comprehending the thermal expansion coefficient variations of multicomponent oxide materials. In summary, judiciously crafted self-attention models overcome tradeoffs between performance and interpretability for materials discovery.Keywords:
Component (thermodynamics)
This article analyses the BP neural network studying and execution algorithm in a single computer, then proposes and uses PVMconstruct neural network in more computers. The implementation of parallel neural network is flexible while being applied to deal with the highdependability and extensive data problems. Besides, the designs and implementation of the BP parallel neural network can use the model to otherneural networks.
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The algorithm GDBSCAN only needs two parameters.It can discover clusters of any shape.But it is very sensitive to the parameter Eps.This paper puts forward a kind of method to make sure the Eps,which is based on datagrid.The method can achieve better result on the case of data distributing unevenly and shapes of clusters reaching each other.
DBSCAN
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A complicated data acquirement method based on XML and component technology is introduced in this paper.In this way, data with complicated structure can be expressed with XML document,so semantic information can be involved in.Through wrapper component and parser component the data can be utilized more easily.To show how to use this method,an example about teaching- evaluation information acquirement is given.
Component (thermodynamics)
XML Schema (W3C)
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The thesis introduce a solution of using DataGrid coned and ComboBox control to realize DataBase of querying
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Scale-invariant feature transform
Component (thermodynamics)
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This paper analyzed the DataGrid Web Server Control. DataGrid is one of the most popular control of ASP .NET which is used to render data to a Web page in tabular form. This paper provides two types of typical usage of DataGrid.
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Autonomic component needs to process various types of request,to improve the efficiency of processing request, the component may has multiple versions of implementation,and for each implementation is optimized for a specific request types.An algorithm for automatic selection of component implementation is provided,the algorithm try to select the most effective implementation for each request,and avoid switching implementation as possible as we can,so the total cost of processing request is reduced.
Component (thermodynamics)
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Бұл зерттеужұмысындaКaно моделітурaлы жәнеоғaн қaтыстытолықмәліметберілгенжәнеуниверситетстуденттерінебaғыттaлғaн қолдaнбaлы (кейстік)зерттеужүргізілген.АхметЯссaуи университетініңстуденттеріүшін Кaно моделіқолдaнылғaн, олaрдың жоғaры білімберусaпaсынa қоятынмaңыздытaлaптaры, яғнисaпaлық қaжеттіліктері,олaрдың мaңыздылығытурaлы жәнесaпaлық қaжеттіліктерінеқaтыстыөз университетінқaлaй бaғaлaйтындығытурaлы сұрaқтaр қойылғaн. Осы зерттеудіңмaқсaты АхметЯсaуи университетіндетуризмменеджментіжәнеқaржы бaкaлaвриaт бaғдaрлaмaлaрыныңсaпaсынa қaтыстыстуденттердіңқaжеттіліктерінaнықтaу, студенттердіңқaнaғaттaну, қaнaғaттaнбaу дәрежелерінбелгілеу,білімберусaпaсын aнықтaу мен жетілдіружолдaрын тaлдaу болыптaбылaды. Осы мaқсaтқaжетуүшін, ең aлдыменКaно сaуaлнaмaсы түзіліп,116 студенткеқолдaнылдыжәнебілімберугежәнеоның сaпaсынa қaтыстыстуденттердіңтaлaптaры мен қaжеттіліктерітоптықжұмыстaрaрқылыaнықтaлды. Екіншіден,бұл aнықтaлғaн тaлaптaр мен қaжеттіліктерКaно бaғaлaу кестесіменжіктелді.Осылaйшa, сaпa тaлaптaры төрт сaнaтқa бөлінді:болуытиіс, бір өлшемді,тaртымдыжәнебейтaрaп.Соңындa,қaнaғaттaну мен қaнaғaттaнбaудың мәндеріесептелдіжәнестуденттердіңқaнaғaттaну мен қaнaғaттaнбaу деңгейлерінжоғaрылaту мен төмендетудеосытaлaптaр мен қaжеттіліктердіңрөліaйқын aнықтaлды.Түйінсөздер:сaпa, сaпaлық қaжеттіліктер,білімберусaпaсы, Кaно моделі.
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The nationally-recognized Susquehanna
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musical level.AÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂA¢AÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂAÂA Experience choral
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