In the quest for technological advancement, materials play a pivotal role. From semiconductors to
the properties of materials define the capabilities of our devices and systems. Traditional
methods of material
discovery, though robust, are time-consuming and expensive. Machine Learning (ML), a
technology that has revolutionized many fields. In the realm of material science, ML-assisted
discovery promises to accelerate the pace of innovation, enabling the development of materials
properties and functionalities.
ML-assisted material discovery operates on a data-driven paradigm. Enormous datasets containing information about the properties, structure, and performance of materials are used to train models. This treasure trove of data encompasses experimental results, simulations, and insights from scientific literature. By mining this information, ML models can uncover hidden patterns and relationships that may elude human researchers.
The heart of ML-assisted material discovery lies in feature engineering. Relevant descriptors are extracted from the data, capturing crucial aspects like elemental composition, crystal structure, electronic properties, and more. These features serve as the foundation upon which predictive models are constructed.
ML models, ranging from linear regressions to complex deep neural networks, are trained to predict material properties based on the extracted features. Through iterative learning, these models become adept at discerning intricate relationships between a material's structure and its performance characteristics.