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Luca Anthony Thiede

4 accepted papers

2026

Coupled Cluster con MoLe: Molecular Orbital Learning for Neural Wavefunctions

Luca Anthony Thiede, Abdulrahman Aldossary, Andreas Burger, Jorge Campos-Gonzalez-Angulo, Alex Zook, Melisa Alkan +5
ICML 2026poster

Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupled cluster (CC) theory is the most successful method for achieving accuracy beyond DFT and predicting properties that cl…

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2026

Derivative Informed Learning of Exchange-Correlation Functionals

Eike S. Eberhard, Luca Anthony Thiede, Abdulrahman Aldossary, Andreas Burger, Nicholas Gao, Vignesh Bhethanabotla +2
ICML 2026poster

Machine-learned (ML) XC-functionals promise improved accuracy, but overfit to training energies and basis sets without proper regularization. We introduce Derivative Informed XC-Loss (DI-Loss), a loss that regularizes ML-XC training by supervising energy gradients on the Grassmannian of density matr…

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2026

Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink

Jonas Elsborg, Felix Aertebjerg, Luca Anthony Thiede, Alan Aspuru-Guzik, Tejs Vegge, Arghya Bhowmik
ICML 2026poster

We introduce ELECTRAFI, a fast, end-to-end differentiable model for predicting periodic charge densities in crystalline materials. ELECTRAFI constructs anisotropic Gaussians in real space and exploits their closed-form Fourier transforms to analytically evaluate plane-wave coefficients via the Poiss…

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2019

Analyzing the Variety Loss in the Context of Probabilistic Trajectory Prediction

Luca Anthony Thiede, Pratik Prabhanjan Brahma
ICCV 2019poster

Trajectory or behavior prediction of traffic agents is an important component of autonomous driving and robot planning in general. It can be framed as a probabilistic future sequence generation problem and recent literature has studied the applicability of generative models in this context. The vari…

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