no code implementations • 4 Feb 2025 • Philipp Hoellmer, Thomas Egg, Maya M. Martirossyan, Eric Fuemmeler, Amit Gupta, Zeren Shui, Pawan Prakash, Adrian Roitberg, Mingjie Liu, George Karypis, Mark Transtrum, Richard G. Hennig, Ellad B. Tadmor, Stefano Martiniani
We benchmark OMG's performance on two tasks: Crystal Structure Prediction (CSP) for specified compositions, and 'de novo' generation (DNG) aimed at discovering stable, novel, and unique structures.
1 code implementation • 27 Sep 2024 • Shivang Rawat, David J. Heeger, Stefano Martiniani
Stability in recurrent neural models poses a significant challenge, particularly in developing biologically plausible neurodynamical models that can be seamlessly trained.
no code implementations • 3 Sep 2024 • 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 Eastman, Stefano Martiniani, Mark E. Tuckerman
A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully enough to get quantitative insights, is among the most ardent dreams of biophysicists -- a dream, nevertheless, not to be fulfilled any time soon.
2 code implementations • 7 Apr 2020 • Stefano Martiniani, Yuval Lemberg, Paul M. Chaikin, Dov Levine
The correlation length and its critical exponents are thus accessible with no a-priori knowledge of an order parameter or even the nature of the ordering.
Statistical Mechanics Disordered Systems and Neural Networks Soft Condensed Matter Cellular Automata and Lattice Gases Computational Physics
1 code implementation • 16 Aug 2017 • Stefano Martiniani, Paul M. Chaikin, Dov Levine
While the equilibrium properties, states, and phase transitions of interacting systems are well described by statistical mechanics, the lack of suitable state parameters has hindered the understanding of non-equilibrium phenomena in diverse settings, from glasses to driven systems to biology.
Soft Condensed Matter Disordered Systems and Neural Networks Statistical Mechanics Computational Physics
no code implementations • 23 Mar 2017 • Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta, Levent Sagun, Jacob D. Stevenson, David J. Wales
Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences.
no code implementations • 19 Dec 2016 • Daan Frenkel, K. Julian Schrenk, Stefano Martiniani
Conventional Monte Carlo simulations are stochastic in the sense that the acceptance of a trial move is decided by comparing a computed acceptance probability with a random number, uniformly distributed between 0 and 1.