Regression-Based Molecule Fragmentation for Energy Accuracy Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The computational complexity of quantum chemical computations for large molecules is high, making it unrealistic to apply algorithms like CCSD(T) and VQE to entire large molecules, and existing methods like DMET face accuracy issues due to varying division patterns in subset division, leading to unpredictable energy calculation accuracy.
Innovation Solution
A regression equation is generated to predict the accuracy of potential energy computation using division patterns, allowing for high-accuracy prediction of energy calculation in large molecules by applying the equation to division candidates, thereby reducing computational complexity and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum chemical computation algorithms (CCSD(T), VQE) are applied to entire large molecules, then accurate molecular properties can be obtained, but computational complexity becomes prohibitively high (O(n^7) for CCSD(T))
Solution Approach 1:
The patent applies segmentation by dividing a large molecule into multiple subsets (fragments) and computing the potential energy of each subset separately. This reduces the computational complexity from O(n^7) for the entire molecule to O(m^7) for each smaller subset, where m < n. The fragments are then combined to obtain the total potential energy of the molecule, making quantum chemical computations feasible for large molecules.
2Device complexity
If DMET method is used to divide molecules into subsets, then computational complexity is reduced, but accuracy becomes unpredictable due to varying division patterns
Solution Approach 1:
The patent changes the parameters used for subset division by introducing multiple division patterns that vary key parameters such as the number of subsets, the size of each subset, and the selection criteria for atoms in each subset. By evaluating accuracy across these varied parameters and selecting optimal division patterns, the method ensures both reduced computational complexity and predictable accuracy.
Solution Approach 2:
The patent implements feedback by evaluating the accuracy of potential energy computation for each division pattern and using this feedback to select the optimal division pattern. The accuracy evaluation results are fed back into the division pattern selection process, allowing the system to iteratively improve and ensure both computational efficiency and accuracy for large molecules.
3Measurement precision
If multiple division patterns are evaluated to ensure accuracy, then computation time increases, but without systematic evaluation accuracy cannot be predicted
Solution Approach 1:
The patent applies preliminary action by pre-establishing multiple division patterns with different parameters before the actual potential energy computation. These pre-defined patterns include variations in subset number, subset size, and atom selection criteria. By having these patterns prepared in advance, the system can efficiently evaluate accuracy without excessive computation time during the actual energy calculation process.
Data Source
AI summary
An information processing apparatus generates a regression equation for predicting the accuracy of computing the potential energy of a first molecule using each of a plurality of division patterns including a plurality of subsets including one or more atoms included in the first molecule. The information processing apparatus applies the regression equation to a plurality of division candidate patterns including a plurality of subsets including one or more atoms included in a second molecule. The information processing apparatus 10 then executes prediction of accuracy of computing the potential energy of the second molecule in the case of using each of the plurality of division candidates.