Stefano Martiniani

According to our database, Stefano Martiniani authored at least 15 papers between 2017 and 2026.

Collaborative distances:

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

2026

PropMolFlow: property-guided molecule generation with geometry-complete flow matching.

Cheng Zeng, Jirui Jin, Connor Ambrose, George Karypis, Mark K. Transtrum, Ellad B. Tadmor, Richard G. Hennig, Adrian Roitberg, Stefano Martiniani, Mingjie Liu

Nat. Comput. Sci., March, 2026

MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching.

Cheng Zeng, Harry W. Sullivan, Thomas Egg, Maya M. Martirossyan, Philipp Höllmer, Jirui Jin, Richard G. Hennig, Adrian Roitberg, Stefano Martiniani, Ellad B. Tadmor, Mingjie Liu

CoRR, February, 2026

Cross-View World Models.

Rishabh Sharma, Gijs Hogervorst, Wayne E. Mackey, David J. Heeger, Stefano Martiniani

CoRR, February, 2026

Open Materials Generation with Inference-Time Reinforcement Learning.

Philipp Höllmer, Stefano Martiniani

CoRR, February, 2026

2025

MolGuidance: Advanced Guidance Strategies for Conditional Molecular Generation with Flow Matching.

Jirui Jin, Cheng Zeng, Pawan Prakash, Ellad B. Tadmor, Adrian Roitberg, Richard G. Hennig, Stefano Martiniani, Mingjie Liu

CoRR, December, 2025

Guided Diffusion for the Discovery of New Superconductors.

Pawan Prakash, Jason B. Gibson, Zhongwei Li, Gabriele Di Gianluca, Juan Esquivel, Eric Fuemmeler, Benjamin Geisler, Jung Soo Kim, Adrian Roitberg, Ellad B. Tadmor, Mingjie Liu, Stefano Martiniani, Gregory R. Stewart, James J. Hamlin, Peter J. Hirschfeld, Richard G. Hennig

CoRR, September, 2025

All that structure matches does not glitter.

Maya M. Martirossyan, Thomas Egg, Philipp Höllmer, George Karypis, Mark K. Transtrum, Adrian Roitberg, Mingjie Liu, Richard G. Hennig, Ellad B. Tadmor, Stefano Martiniani

CoRR, September, 2025

A unifying approach to self-organizing systems interacting via conservation laws.

Frank Barrows, Guanming Zhang, Satyam Anand, Zixi Chen, Jonathan Lin, Aman Desai, Stefano Martiniani, Francesco Caravelli

CoRR, July, 2025

A practical guide to machine learning interatomic potentials - Status and future.

Ryan Jacobs, Dane Morgan, Siamak Attarian, Jun Meng, Chen Shen, Zhenghao Wu, Clare Yijia Xie, Julia H. Yang, Nongnuch Artrith, Ben Blaiszik, Gerbrand Ceder, Kamal Choudhary, Gábor Csányi, Ekin Dogus Cubuk, Bowen Deng, Ralf Drautz, Xiang Fu, Jonathan Godwin, Vasant G. Honavar, Olexandr Isayev, Anders Johansson, Boris Kozinsky, Stefano Martiniani, Shyue Ping Ong, Igor Poltavsky, K. J. Schmidt, So Takamoto, Aidan Thompson, Julia Westermayr, Brandon M. Wood

CoRR, March, 2025

Perspective on artificial intelligence for accelerated materials design (AI4Mat) workshops in 2024.

Santiago Miret, Marta Skreta, Geemi P. Wellawatte, Stefano Martiniani, N. M. Anoop Krishnan, George Karypis, Kevin Maik Jablonka

Mach. Learn. Sci. Technol., 2025

Contrastive Self-Supervised Learning As Neural Manifold Packing.

Guanming Zhang, David J. Heeger, Stefano Martiniani

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Open Materials Generation with Stochastic Interpolants.

Philipp Höllmer, Thomas Egg, Maya M. Martirossyan, Eric Fuemmeler, Zeren Shui, Amit Gupta, Pawan Prakash, Adrian Roitberg, Mingjie Liu, George Karypis, Mark K. Transtrum, Richard G. Hennig, Ellad B. Tadmor, Stefano Martiniani

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

2024

On the design space between molecular mechanics and machine learning force fields.

Yuanqing Wang, Kenichiro Takaba, Michael S. Chen, Marcus Wieder, Yuzhi Xu, Tong Zhu, John Z. H. Zhang, Arnav Nagle, Kuang Yu, Xinyan Wang, Daniel J. Cole, Joshua A. Rackers, Kyunghyun Cho, Joe G. Greener, Peter K. Eastman, Stefano Martiniani, Mark E. Tuckerman

CoRR, 2024

Unconditional stability of a recurrent neural circuit implementing divisive normalization.

Shivang Rawat, David J. Heeger, Stefano Martiniani

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2017

Perspective: Energy Landscapes for Machine Learning.

Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta, Levent Sagun, Jacob D. Stevenson, David J. Wales

CoRR, 2017