System and Method for Prediction of Molecular Dynamics and Chemical Properties Using Equivariant Neural Operators

Patent metadata

Field	Value
Publication number	US-2024304285-A1
Application number	US-202218552362-A
Country	US
Kind code	A1
Filing date	Mar 29, 2022
Priority date	Mar 29, 2021
Publication date	Sep 12, 2024
Grant date	—

Abstract

Disclosed herein is a system and method using an equivariant neural network for predicting quantum mechanical charge density. The equivariant neural network serves as a surrogate for the density-functional theory used to calculate a selfconsistent field and predicts the central observable charge density, which, in addition to enabling force calculations, can also accelerate DFT itself and compute a full range of chemical properties.

First claim

1 . A method comprising:
inputting a set of atomic moieties and positions to a neural network; receiving, from the neural network, an initial charge density distribution; and inputting the initial charge density distribution to a density functional theory engine to calculate a self-consistent field.

2 . The method of claim 1 further comprising:
repeatedly inputting the self-consistent field to the density functional theory engine until density functional theory convergence is achieved.

3 . The method of claim 1 wherein the set of atomic moieties and positions is in the form of an array of scalars dependent upon the atomic moiety.

4 . The method of claim 1 wherein the neural network is an equivariant neural network outputting a set of tensor features representing the charge density distribution at each atomic position.

5 . The method of claim 4 wherein the set of tensor features is computed at each atomic position via an equivariant convolution.

6 . The method of claim 3 wherein the equivariant neural network is equivariant in terms of rotation and translation.

7 . The method of claim 5 further comprising inputting the set of tensor features to a scaler neural network to predict charge density at each atomic position.

8 . The method of claim 1 further comprising
inputting the charge density distribution produced by the equivariant neural network to one or more property calculators.

9 . The method of claim 8 wherein the one or more property calculators include a forces calculator and a multipole moments calculator.

10 . The method of claim 6 wherein the equivariant neural network implements an equivariant operator operating between sets of tensor fields.

11 . The method of claim 10 wherein the equivariant operator is implemented as a composition of a tensor field convolution linear layer, a local product by linear layer and a local nonlinear layer.

12 . The method of claim 11 wherein the output of the equivariant neural network is a convolution of the input and a characteristic impulse response.

13 . The method of claim 12 wherein the impulse response is a product of a scaler radial function and a spherical harmonic, such that the equivariant neural network is rotationally equivariant.

14 . The method of claim 6 wherein the equivariant operator maps a scalar field of electronic density to a scalar quantity of total energy.

15 . The method of claim 14 further comprising:
iteratively refining the initial charge density distribution via gradient descent.

16 . The method of claim 15 wherein external nuclear potential, mean field, and approximate exchange-correlation energies are directly calculated.

17 . The method of claim 16 wherein a trainable portion of the equivariant operator learns a deviation of the gradient descent.

18 . The method of claim 17 wherein the operator is trained to predict energy and have its functional derivative approach a zero field with respect to charge density distribution.

19 . A system comprising:
a processor; and software, which, when executed on the processor, causes the system to perform the functions of:
inputting a set of atomic moieties and positions to an equivariant neural network; receiving, from the equivariant neural network, an initial charge density distribution; and inputting the initial charge density distribution to a density functional theory engine to calculate a self-consistent field.

20 . The system of claim 19 the software further causing the system to perform the functions of: repeatedly inputting the self-consistent field to the density functional theory engine until density functional theory convergence is achieved.

21 . The system of claim 19 wherein the set of atomic moieties and positions is in the form of an array of scalars dependent upon the atomic moiety, and further wherein the equivariant neural network outputs a set of tensor features representing the charge density distribution at each atomic position.

22 . The system of claim 19 wherein the equivariant neural network is equivariant in terms of rotation and translation.

23 . The system of claim 19 , the software further causing the system to:
input the charge density distribution produced by the equivariant neural network to one or more property calculators, the one or more property calculus including a forces calculator and a multipole moments calculator.

24 . The system of claim 19 wherein the equivariant neural network implements an equivariant operator operating between sets of tensor fields.

25 . The system of claim 24 wherein the equivariant operator is implemented as a composition of a tensor field convolution linear layer, a local product by linear layer and a local nonlinear layer.

26 . The system of claim 24 wherein the equivariant operator maps a scalar field of electronic density to a scalar quantity of total energy.

Assignees

• Univ Carnegie Mellon

Inventors

• Shen Xingping
• Viswanathan Venkatasubramanian

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G16C20/70

  Machine learning, data mining or chemometrics · CPC title

• G06N3/084

  Backpropagation, e.g. using gradient descent · CPC title

• G16C20/30 Primary

  Prediction of properties of chemical compounds, compositions or mixtures · CPC title