Effects of Machine Learning Algorithms for Predicting and Optimizing the Properties of New Materials in the United States

Purpose: The aim of this study is to investigate the effects of machine learning algorithms in predicting and optimizing the properties of new materials in the United States. Materials and Methods: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low-cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Results: The research found that machine learning algorithms have a significant impact on materials prediction and optimization in the United States, particularly in energy storage, catalysis, electronics, and aerospace. These algorithms offer advantages in efficiency, scalability, and accuracy compared to traditional methods, but challenges such as data quality, scarcity, interpretability, and reliability need to be addressed to ensure robust and reliable predictions. Recommendations:  This study contributes to the understanding of the effects of machine learning algorithms in predicting and optimizing the properties of new materials in the United States. The research advances the knowledge in the field of materials science, materials prediction, and materials optimization. The findings provide insights into the potential of machine learning algorithms for accelerating materials discovery and innovation, and highlight the challenges and opportunities in their application for materials prediction and optimization. The study has practical implications for researchers, engineers, and policymakers involved in materials science, materials design, and materials innovation. The research underscores the importance of leveraging machine learning algorithms as a powerful tool for materials prediction and optimization, and emphasizes the need for further research, development, and integration of these techniques in materials science and engineering practices.