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METHODS FOR PREDICTING LIKELIHOOD OF SUCCESSFUL EXPERIMENTAL SYNTHESIS OF COMPUTER-GENERATED MATERIALS BY COMBINING NETWORK ANALYSIS AND MACHINE LEARNING
METHODS FOR PREDICTING LIKELIHOOD OF SUCCESSFUL EXPERIMENTAL SYNTHESIS OF COMPUTER-GENERATED MATERIALS BY COMBINING NETWORK ANALYSIS AND MACHINE LEARNING
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机译:网络分析与机器学习相结合的成功模拟计算机生成材料的模拟方法
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摘要
One aspect of the disclosure relates to systems and methods for determining probabilities of successful synthesis of materials in the real world at one or more points in time. The probabilities of successful synthesis of materials in the real world at one or more points in time can be determined by representing the materials and their pre-defined relationships respectively as nodes and edges in a network form, and computation of the parameters of the nodes in the network as input to a classification model for successful synthesis. The classification model being configured to determine probabilities of successful synthesis of materials in the real world at one or more points in time.
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