Ensemble Neural Network for Chemical Property Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In the fields of fragrance design, perfumery, and flavor design, the scarcity of abundant datasets poses challenges for neural network technologies, leading to inefficient models due to the risk of memorization and the need for reducing the number of parameters, as well as difficulties in training and combining ensemble models effectively.
Innovation Solution
An end-to-end trained ensemble neural network or multi-branch neural network model is used to predict physico-chemical and odor property values, employing digitized representations of chemical structures or compositions, with a sampling device to output random values based on the distribution of independent predictions, allowing for more stable and reliable predictions with reduced model complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional neural network models are used with limited datasets, then the model may memorize the training data, but the model complexity and number of parameters increase
Solution Approach 1:
The patent segments the neural network into an ensemble of multiple independent sub-models (e.g., 10-100 models) rather than using a single complex model. Each sub-model processes the input independently and their predictions are aggregated, reducing the complexity of individual models while maintaining or improving overall prediction reliability through ensemble diversity
Solution Approach 2:
The patent combines multiple independent neural network models into an ensemble system where predictions from individual models are merged through aggregation functions (mean, median, weighted average). This merging approach improves reliability by reducing variance and preventing memorization while keeping each individual model simpler
2Reliability
If the number of parameters in the neural network is reduced to match limited data size, then the risk of memorization decreases, but the prediction accuracy may be insufficient
Solution Approach 1:
The patent combines predictions from multiple independent models to achieve higher accuracy than any single model could provide. The aggregation of diverse predictions through ensemble methods improves measurement precision while each individual model maintains reduced parameter counts to avoid memorization
Solution Approach 2:
The patent changes the parameters of the ensemble system (number of models, aggregation method, random seed variations) to optimize the balance between prediction accuracy and reliability. By adjusting ensemble size and composition, the system achieves high accuracy without requiring each individual model to have excessive parameters
3Adaptability or versatility
If ensemble models are trained and combined using traditional methods, then diverse predictions can be obtained, but the training process becomes complex and requires extensive hyperparameter optimization
Solution Approach 1:
The patent implements self-service mechanisms where the ensemble models automatically generate diverse predictions through random initialization and independent training on the same data. The system self-organizes the diversity without requiring complex external hyperparameter optimization or manual intervention to create varied training conditions
Solution Approach 2:
The patent performs preliminary actions by pre-defining the ensemble architecture and training protocol before actual training begins. Models are pre-configured with different random seeds and architectural variations, allowing them to naturally develop diverse predictions during training without requiring complex post-training combination strategies
Data Source
AI summary
The method (100) to predict physico-chemical and/or odor properties value for chemical structures or compositions comprises the steps of:defining (105) a representation of a chemical structure or composition,executing (110) upon the representation defined, an end-to-end trained ensemble neural network or multi-branch neural network model to predict a physico-chemical and/or odor property value,providing (115) the physico-chemical and/or odor property value,the method further comprising:providing (120) exemplar data to an end-to-end ensemble neural network or multi-branch neural network device comprising:several neural network sub-devices configured to independent predictions,a layer to output at least one value of the distribution of independent predictions andsaid layer comprising a sampling device configured to output random values,operating (125) the end-to-end ensemble neural network or multi-branch neural network device andobtaining (130) the trained ensemble neural network or multi-branch neural network model.