AI for Materials Discovery and Sustainable Design

Module: Sustainable Tech | Difficulty: Premium

Material Property Prediction

The Young's modulus follows the rule of mixtures:

The Halpin-Tsai model for nanoparticle composites:

where .

Comparison

Python Implementation

import numpy as np
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel

class MaterialsDiscovery:
    def __init__(self):
        kernel = ConstantKernel(1.0) * RBF(length_scale=1.0)
        self.model = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=10)

    def predict_property(self, X_train, y_train, X_test):
        self.model.fit(X_train, y_train)
        return self.model.predict(X_test, return_std=True)

    def acquisition_function(self, X_train, y_train, X_candidates):
        mean, std = self.predict_property(X_train, y_train, X_candidates)
        return mean + 1.96 * std

Research Insight: Graph neural networks trained on the Materials Project database can predict formation energies with MAE below 0.05 eV/atom.