Emine Küçükbenli

According to our database, Emine Küçükbenli authored at least 14 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute.

Kieran Didi, Zuobai Zhang, Guoqing Zhou, Danny Reidenbach, Zhonglin Cao, Sooyoung Cha, Tomas Geffner, Christian Dallago, Jian Tang, Michael M. Bronstein, Martin Steinegger, Emine Küçükbenli, Arash Vahdat, Karsten Kreis

CoRR, March, 2026

Fold-CP: A Context Parallelism Framework for Biomolecular Modeling.

Dejun Lin, Simon K. S. Chu, Vishanth Iyer, Youhan Lee, John St. John, Kevin Boyd, Brian Roland, Xiaowei Ren, Guoqing Zhou, Zhonglin Cao, Polina Binder, Yuliya Zhautouskaya, Jakub Zakrzewski, Maximilian Stadler, Kyle Gion, Yuxing Peng, Xi Chen, Tianjing Zhang, Philipp Junk, Michelle Dimon, Pawel Gniewek, Fabian Ortega, McKinley Polen, Ivan Grubisic, Ali Bashir, Graham Holt, Danny Kovtun, Matthias Grass, Luca Naef, Rui Wang, Jian Peng, Anthony B. Costa, Saee Paliwal, Eddie Calleja, Timur Rvachov, Neha Tadimeti, Roy Tal, Emine Küçükbenli

CoRR, March, 2026

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials.

Cong Fu, Yuchao Lin, Zachary Krueger, Haiyang Yu, Maho Nakata, Jianwen Xie, Emine Küçükbenli, Xiaofeng Qian, Shuiwang Ji

Trans. Mach. Learn. Res., 2026

2025

La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching.

Tomas Geffner, Kieran Didi, Zhonglin Cao, Danny Reidenbach, Zuobai Zhang, Christian Dallago, Emine Küçükbenli, Karsten Kreis, Arash Vahdat

CoRR, July, 2025

ToxBench: A Binding Affinity Prediction Benchmark with AB-FEP-Calculated Labels for Human Estrogen Receptor Alpha.

Meng Liu, Karl Leswing, Simon K. S. Chu, Farhad Ramezanghorbani, Griffin Young, Gabriel Marques, Prerna Das, Anjali Panikar, Esther Jamir, Mohammed Sulaiman Shamsudeen, K. Shawn Watts, Ananya Sen, Hari Priya Devannagari, Edward B. Miller, Muyun Lihan, Howook Hwang, Janet Paulsen, Xin Yu, Kyle Gion, Timur Rvachov, Emine Küçükbenli, Saee Gopal Paliwal

CoRR, July, 2025

Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations.

Yuchao Lin, Cong Fu, Zachary Krueger, Haiyang Yu, Maho Nakata, Jianwen Xie, Emine Küçükbenli, Xiaofeng Qian, Shuiwang Ji

CoRR, July, 2025

Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow.

Zhonglin Cao, Mario Geiger, Allan dos Santos Costa, Danny Reidenbach, Karsten Kreis, Tomas Geffner, Franco Pellegrini, Guoqing Zhou, Emine Küçükbenli

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Proteina: Scaling Flow-based Protein Structure Generative Models.

Tomas Geffner, Kieran Didi, Zuobai Zhang, Danny Reidenbach, Zhonglin Cao, Jason Yim, Mario Geiger, Christian Dallago, Emine Küçükbenli, Arash Vahdat, Karsten Kreis

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE.

Jesun Sahariar Firoz, Franco Pellegrini, Mario Geiger, Darren Hsu, Jenna A. Bilbrey, Han-Yi Chou, Maximilian Stadler, Markus Höhnerbach, Tingyu Wang, Dejun Lin, Emine Küçükbenli, Henry W. Sprueill, Ilyes Batatia, Sotiris S. Xantheas, MalSoon Lee, Christopher J. Mundy, Gábor Csányi, Justin S. Smith, Ponnuswamy Sadayappan, Sutanay Choudhury

Proceedings of the 34th International Symposium on High-Performance Parallel and Distributed Computing, 2025

2024

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery.

Peter St. John, Dejun Lin, Polina Binder, Malcolm Greaves, Vega Shah, John St. John, Adrian Lange, Patrick D. Hsu, Rajesh Illango, Arvind Ramanathan, Anima Anandkumar, David H. Brookes, Akosua Busia, Abhishaike Mahajan, Stephen Malina, Neha Prasad, Sam Sinai, Lindsay Edwards, Thomas Gaudelet, Cristian Regep, Martin Steinegger, Burkhard Rost, Alexander Brace, Kyle Hippe, Luca Naef, Keisuke Kamata, George Armstrong, Kevin Boyd, Zhonglin Cao, Han-Yi Chou, Simon Chu, Allan dos Santos Costa, Sajad Darabi, Eric Dawson, Kieran Didi, Cong Fu, Mario Geiger, Michelle Gill, Darren Hsu, Gagan Kaushik, Maria Korshunova, Steven Kothen-Hill, Youhan Lee, Meng Liu, Micha Livne, Zachary McClure, Jonathan Mitchell, Alireza Moradzadeh, Ohad Mosafi, Youssef Nashed, Saee Paliwal, Yuxing Peng, Sara Rabhi, Farhad Ramezanghorbani, Danny Reidenbach, Camir Ricketts, Brian Roland, Kushal Shah, Tyler Shimko, Hassan Sirelkhatim, Savitha Srinivasan, Abraham C. Stern, Dorota Toczydlowska, Srimukh Prasad Veccham, Niccolò Alberto Elia Venanzi, Anton Vorontsov, Jared Wilber, Isabel Wilkinson, Wei Jing Wong, Eva Xue, Cory Ye, Xin Yu, Yang Zhang, Guoqing Zhou, Becca Zandstein, Christian Dallago, Bruno Trentini, Emine Küçükbenli, Timur Rvachov, Eddie Calleja, Johnny Israeli, Harry Clifford, Risto Haukioja, Nicholas Haemel, Kyle Tretina, Neha Tadimeti, Anthony B. Costa

CoRR, 2024

EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants.

Allan dos Santos Costa, Ilan Mitnikov, Franco Pellegrini, Ameya Daigavane, Mario Geiger, Zhonglin Cao, Karsten Kreis, Tess E. Smidt, Emine Küçükbenli, Joseph Jacobson

CoRR, 2024

LATTE: an atomic environment descriptor based on Cartesian tensor contractions.

Franco Pellegrini, Stefano de Gironcoli, Emine Küçükbenli

CoRR, 2024

2023

PANNA 2.0: Efficient neural network interatomic potentials and new architectures.

Franco Pellegrini, Ruggero Lot, Yusuf Shaidu, Emine Küçükbenli

CoRR, 2023

2020

PANNA: Properties from Artificial Neural Network Architectures.

Ruggero Lot, Franco Pellegrini, Yusuf Shaidu, Emine Küçükbenli

Comput. Phys. Commun., 2020