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)

Unconditional stability of a recurrent neural circuit implementing divisive normalization. - Shivang Rawat; David J. Heeger; Stefano Martiniani (NeurIPS, 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; et al. (CoRR, 2024)

Open Materials Generation with Stochastic Interpolants. - Philipp Höllmer; Thomas Egg; Maya M. Martirossyan; Eric Fuemmeler; Amit Gupta; Zeren Shui; Pawan Prakash; Adrian Roitberg; Mingjie Liu; George Karypis; Mark K. Transtrum; Richard G. Hennig; et al. (CoRR, 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; et al. (CoRR, 2025)

Contrastive Self-Supervised Learning As Neural Manifold Packing. - Guanming Zhang; David J. Heeger; Stefano Martiniani (CoRR, 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, 2025)