Systems and methods for prediction of polymer properties

1. A method for predicting polymer properties comprising:

analyzing a first polymer using a deep learning neural network to analyze a first set of functional groups of the first polymer, the deep learning neural network comprising a merged layer formed from the concatenation of a property branch and a polymer branch;

determining a value from the merged layer; and

outputting the value.

2. The method of claim 1 further comprising submitting the first polymer to a polymer prediction system;

wherein the polymer prediction system comprises the deep learning neural network;

wherein the determined value is indicative of a property of the first polymer;

wherein outputting the value comprises outputting the value to a user of the polymer prediction system;

wherein the property branch is a solubility branch; and

wherein the property is a solubility of the first polymer in a first solvent.

3. The method of claim 2 , wherein the value is indicative of the first solvent being a good solvent.

4. The method of claim 2 , wherein the value is indicative of the first solvent being a nonsolvent.

5. The method of claim 2 , wherein the determining further comprises determining;

if the value is above a first predetermined threshold; and

if the value is below a second predetermined threshold.

6. The method of claim 1 further comprising training the deep learning neural network using a training dataset comprising a plurality of known polymers and a plurality of descriptors for each of the plurality of known polymers.

7. The method of claim 1 , wherein the property branch comprises two or more layers, each layer having from 1 to 100 neurons.

8. The method of claim 1 , wherein the polymer branch comprises two or more layers, each layer having from 1 to 100 neurons.

9. The method of claim 1 , wherein:

one or both:

the property branch comprises two or more layers; and

the polymer branch comprises two or more layers;

each layer has from 1 to 100 neurons; and

each layer is constructed with a parameterized rectified linear unit.

10. The method of claim 1 , wherein the merged layer comprises a sigmoid activation function.

11. A deep learning neural network comprising:

a property branch comprising two or more layers, each layer having a plurality of neurons;

a polymer branch comprising two or more layers, each layer having a plurality neurons; and

a merged layer including a concatenation operation, the concatenation operation configured to concatenate the property branch and the polymer branch.

12. A polymer prediction system comprising:

the deep learning neural network of claim 11 ;

wherein the deep learning neural network further comprises a training dataset comprising a plurality of known polymers and a plurality of descriptors for each of the plurality of known polymers; and

wherein the system is configured to:

receive an inputted first polymer having a first set of functional groups;

analyze the first polymer using the deep learning neural network;

determine a value for the first polymer, the value being indicative of a property of the first polymer; and

output the property and the value to a user of the polymer prediction system.

13. The system of claim 12 , wherein the property branch is a solubility branch; and

wherein the property is a solubility of the first polymer in a first solvent.

14. The system of claim 12 , wherein the value is indicative of the first solvent being a good solvent.

15. The system of claim 12 , wherein the value is indicative of the first solvent being a nonsolvent.

16. The system of claim 12 , wherein the system is further configured to determine;

if the value is above a first predetermined threshold; and

if the value is below a second predetermined threshold.

17. The deep learning neural network of claim 11 , wherein each of the two or more layers in the property branch are constructed with a parameterized rectified linear unit.

18. The deep learning neural network of claim 11 , wherein each of the two or more layers in the polymer branch are constructed with a parameterized rectified linear unit.

19. The deep learning neural network of claim 11 , wherein the deep learning neural network has a latent space dimensionality selected from 3 to 10.

20. The deep learning neural network of claim 11 , wherein the merged layer further comprises a sigmoid activation function.