The integration of the Density Functional Theory (DFT) with machine learning presents promising avenues for accelerating electronic structure applications, notably in silico materials discovery and the exploration of novel chemical reaction pathways [1]. This synergistic fusion extends its applicability across diverse domains, encompassing Dry Reforming, Steam Reforming, and Partial Oxidation, Auto thermal Reforming, Gasification, and Methane Pyrolysis [2]. Leveraging machine learning methodologies alongside these processes can effectively mitigate resource constraints and facilitate simulations of expansive systems, thereby assuming a pivotal role in addressing critical scientific and technological challenges. The realization of large-scale electronic structure simulations owes its feasibility to the advent of contemporary, high-performance computational resources [1]. Additionally, by elucidating electronic structure dynamics, novel materials can be engineered to offer substantial optimization and enhancements, particularly in industrial and multidisciplinary sectors, thereby reshaping material utilization paradigms and performance benchmarks.

The research objectives for this Ph.D. proposal encompass the following:

1. Integrate Density Functional Theory (DFT) with machine learning to significantly speed up electronic structure calculations.
2. Innovate Material Discovery.
3. Enhance Industrial Processes.

To achieve the research objectives, the following methodology will be employed:

1. Data Collection: Gather extensive datasets from DFT calculations for various materials.
2. Develop and train machine learning models using the collected data to predict electronic structure properties.
3. Employ advanced algorithms such as neural networks, support vector machines, and Gaussian processes.
4. Use machine learning models to accelerate the convergence of DFT calculations and to predict properties for large, complex systems.
5. Validate the integrated approach with experimental data and known benchmarks.

The proposed research aims to significantly advance the field of material sciences. The expected outcomes of this research include:

1. Significant reduction in computation time for electronic structure simulations.
2. Identification of novel materials with enhanced properties for industrial applications.
3. Advancement in the field of computational chemistry and materials science.
4. Contribution to the development of next-generation computational tools and methodologies for material and chemical research.

[1] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, PHYSICAL REVIEW MATERIALS 6, 040301, (2022).

[2] L. I. Ugwu, Y. Morgan, H. Ibrahim, International Journal of Hydrogen Energy 47, 2245-2267, (2022).