115 citations · 121 across the 6 of their papers we have counts for
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Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations
Karan Shah, Attila Cangi
Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under external time-dependent perturbations such as laser fields. In this…
Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
Attila Cangi, Lenz Fiedler, Bartosz Brzoza +11
We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for…
Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators
Karan Shah, Attila Cangi
Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under various external perturbations such as laser fields. In this work, w…
A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
Lenz Fiedler, Karan Shah, Michael Bussmann +1
With the growth of computational resources, the scope of electronic structure simulations has increased greatly. Artificial intelligence and robust data analysis hold the promise t…