activity
20232026
most citedEquivariant Message Passing Neural Network for Crystal Material Discovery

3 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LO2026

A Fast Model Counting Algorithm for Two-Variable Logic with Counting and Modulo Counting Quantifiers

Shixin Sun, Astrid Klipfel, Ondřej Kuželka +2

Weighted first-order model counting (WFOMC) is a central task in lifted probabilistic inference: It asks for the weighted sum of all models of a first-order sentence over a finite…

cs.LG2026

Fourier Transformers for Latent Crystallographic Diffusion and Generative Modeling

Jed A. Duersch, Elohan Veillon, Astrid Klipfel +2

The discovery of new crystalline materials calls for generative models that handle periodic boundary conditions, crystallographic symmetries, and physical constraints, while scalin…

cond-mat.mtrl-sci2025

A Chemically Grounded Evaluation Framework for Generative Models in Materials Discovery

Elohan Veillon, Astrid Klipfel, Adlane Sayede +1

Generative models hold great promise for accelerating materials discovery, but their evaluation often overlooks the chemical validity and stability requirements crucial to real-wor…

cond-mat.mtrl-sci2023★ 1 cited

Vector Field Oriented Diffusion Model for Crystal Material Generation

Astrid Klipfel, Yaël Fregier, Adlane Sayede +1

Discovering crystal structures with specific chemical properties has become an increasingly important focus in material science. However, current models are limited in their abilit…

cond-mat.mtrl-sci2023

Optimized Crystallographic Graph Generation for Material Science

Astrid Klipfel, Yaël Frégier, Adlane Sayede +1

Graph neural networks are widely used in machine learning applied to chemistry, and in particular for material science discovery. For crystalline materials, however, generating gra…

cond-mat.mtrl-sci2023★ 1 cited

Unified Model for Crystalline Material Generation

Astrid Klipfel, Yaël Frégier, Adlane Sayede +1

One of the greatest challenges facing our society is the discovery of new innovative crystal materials with specific properties. Recently, the problem of generating crystal materia…

cs.LG2023★ 3 cited

Equivariant Message Passing Neural Network for Crystal Material Discovery

Astrid Klipfel, Olivier Peltre, Najwa Harrati +3

Automatic material discovery with desired properties is a fundamental challenge for material sciences. Considerable attention has recently been devoted to generating stable crystal…