We develop the Materials Learning Algorithms (MALA), a physics-informed machine learning framework that aims to accelerate conventional density functional theory simulations. Using neural networks, MALA efficiently computes the electronic structure of matter, enabling accurate determination of energies and forces that are critical for atomistic simulations. MALA is a scalable method that balances accuracy and speed, overcoming the limitations of conventional density functional theory simulations. It combines the scalability of atomistic simulations with the high accuracy of first-principles methods, paving the way for electronic structure calculations at unprecedented length and time scales. This advancement in materials modeling opens up a broad range of potential applications.

We bridge the divide between atomistic simulations at the microscopic scale and material simulations at the mesoscopic scale. By combining high-fidelity data generation with machine-learning models, we create interatomic potentials for high-performance molecular-spin dynamics simulations. This methodology couples lattice degrees of freedom with electronic spins. We utilize this method for tackling a wide range of applications, including the analysis of material strength, the investigation of transport properties in nanoscale systems, and the simulation of magneto-structural phase transitions relevant for developing ultrafast magnetic storage devices.

We use advanced simulation methods, such as time-dependent density functional theory, to model how electrons in materials respond to optical laser light. This enables us to predict important material properties, including response functions and electronic transport behavior, which are essential for designing next-generation photonic and nanoelectronic devices.

We develop advanced electronic structure methods such as density functional theory for modeling both static and dynamic material properties. This includes creating novel exchange-correlation approximations for ground-state density functional theory (DFT), and applying time-dependent density functional theory (TD-DFT) for dynamic phenomena. We also integrate artificial intelligence techniques to enhance the accuracy and efficiency of our DFT approaches.