Automated Active-Space Selection From Molecular Orbital Occupancy Data
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Solution Overview
Problem
The determination of active space in quantum chemical calculations is subjective and lacks a definite rule, affecting the accuracy and efficiency of calculations, making it difficult to balance calculation resources and time without specialized knowledge.
Innovation Solution
An information output program that applies principal component analysis to occupancy number data from molecular orbitals to automatically determine an active space, enhancing calculation accuracy and resource management by identifying orbitals with significant influence on quantum chemical calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If active space is determined by subjective judgment, then expert knowledge can guide calculation accuracy, but the process becomes complex and difficult to automate
Solution Approach 1:
The patent replaces the mechanical process of expert subjective judgment with an automated computational system using machine learning models. The system processes molecular orbital data through trained algorithms to objectively determine active space, eliminating the need for expert intervention while maintaining or improving determination accuracy.
Solution Approach 2:
The system enables the quantum chemical calculation process to determine its own active space parameters automatically. By using machine learning models trained on molecular orbital data, the system performs self-determination of active space without external expert input, making the process autonomous and repeatable.
2Measurement precision
If all molecular orbitals are used for calculation, then calculation accuracy is maximized, but calculation time and resource consumption increase significantly
Solution Approach 1:
The patent extracts only the essential molecular orbitals that constitute the active space from the complete set of molecular orbitals. By identifying and selecting specific orbitals based on their contribution to the chemical bond and reactivity, the system reduces the calculation scope to only those orbitals that matter, maintaining accuracy while improving efficiency.
Solution Approach 2:
The patent segments the complete set of molecular orbitals into relevant active space orbitals and irrelevant orbitals. This segmentation allows the calculation to focus only on the segmented subset of orbitals that contribute significantly to the chemical properties being studied, reducing computational burden while preserving essential information.
3Ease of operation
If active space is determined using conventional methods, then the process is simple, but the results lack objectivity and reproducibility
Solution Approach 1:
The patent replaces subjective expert judgment with an objective machine learning-based determination system. The trained models process molecular orbital data through consistent algorithms, producing reproducible results that are independent of individual expert preferences while maintaining scientific rigor.
Solution Approach 2:
The patent transforms the active space determination from a subjective parameter selection process to an objective data-driven process. By changing the determination criteria from expert opinion to machine learning predictions based on molecular orbital characteristics, the system achieves both objectivity and reproducibility.
Data Source
AI summary
A non-transitory computer-readable recording medium storing an information output program for causing a computer to execute a process includes acquiring occupancy number data that includes a time series of occupancy numbers for each of a plurality of molecular orbitals, executing principal component analysis on the occupancy number data, and outputting information on an active space that corresponds to a subset used for a quantum chemical calculation, among the plurality of molecular orbitals, based on a result of the principal component analysis.