115 citations · 121 across the 6 of their papers we have counts for
6 papers
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…
Inverting the Kohn-Sham equations with physics-informed machine learning
Vincent Martinetto, Karan Shah, Attila Cangi +1
Electronic structure theory calculations offer an understanding of matter at the quantum level, complementing experimental studies in materials science and chemistry. One of the mo…
Physics-Informed Neural Networks as Solvers for the Time-Dependent Schrödinger Equation
Karan Shah, Patrick Stiller, Nico Hoffmann +1
We demonstrate the utility of physics-informed neural networks (PINNs) as solvers for the non-relativistic, time-dependent Schrödinger equation. We study the performance and genera…
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…