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Solving the Schrodinger equation using program synthesis
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AI
DOI：10.1063/5.0062497.png)
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Abstract
En 中文
We demonstrate that a program synthesis approach based on a linear code representation can be used to generate algorithms that approximate the ground-state solutions of one-dimensional time-independent Schrodinger equations constructed with bound polynomial potential energy surfaces (PESs). Here, an algorithm is constructed as a linear series of instructions operating on a set of input vectors, matrices, and constants that define the problem characteristics, such as the PES. Discrete optimization is performed using simulated annealing in order to identify sequences of code-lines, operating on the program inputs that can reproduce the expected ground-state wavefunctions psi(x) for a set of target PESs. The outcome of this optimization is not simply a mathematical function approximating psi(x) but is, instead, a complete algorithm that converts the input vectors describing the system into a ground-state solution of the Schrodinger equation. These initial results point the way toward an alternative route for developing novel algorithms for quantum chemistry applications.
Keywords:
NEURAL-NETWORK
COUPLED-CLUSTER
SMALL MOLECULES
DATABASE
APPROXIMATION
TUTORIAL
MODEL
Journal
IF:
3.1
Papers:
7.2W
Citations:
23.2W
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