203 citations · 261 across the 21 of their papers we have counts for
27 papers
Reinforcement Learning on the Discrete Composition Channel of a Crystal Generator: Validated Gains and Reward Hacking
Pawan Prakash, Philipp Höllmer, Addis Fuhr +4
Inverse materials design is a long-standing goal of computational materials discovery. Generative models for crystalline materials are typically trained to match the distribution o…
Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction
Thomas Egg, Harry Winston Sullivan, Maya M. Martirossyan +8
Crystal structure governs material properties, making crystal structure prediction (CSP) a fundamental problem in materials science. Generative models are a promising approach for…
OMatG-flash: An All-Atom Flow Map with Reinforce Adjoint Matching for Scalable Materials Discovery
Thomas Egg, Harry Winston Sullivan, Ellad B. Tadmor +1
The discovery of novel inorganic materials drives technological breakthroughs in critical fields such as computing and energy storage. Generative AI has promised to accelerate the…
A minimal mechanism to generate long timescales without fine tuning
Kathryn McClain, Shivang Rawat, Mia Morrell +3
Long timescales in brain dynamics give rise to power law correlations measured in experiments. A simple linear recurrent neural network model can reproduce this power law behavior,…
AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis
Bing Yan, Gregory Wolfe, Stefano Martiniani +1
Chemistry literature synthesis often requires assembling specific findings scattered across many publications, yet existing literature-search systems primarily return ranked docume…
Hyperuniform systems are maximally irreversible
Mathias Casiulis, Satyam Anand, Stefano Martiniani
Hyperuniform systems, defined by the anomalous suppression of large-scale density fluctuations, are a paradigm of non-equilibrium self-assembly. While mechanisms underlying the sel…
Cyclic Denoising Reveals Ultrastable Memories in Diffusion Models
Rishabh Sharma, Stefano Martiniani
We introduce cyclic denoising -- repeated forward and reverse diffusion at controlled noise amplitudes -- as an extraction attack for image diffusion models. Inspired by random org…
Exact Variance and Fano Factor for Arbitrary Level Crossings in Stationary Gaussian Processes
Shivang Rawat, Flaviano Morone, David J. Heeger +1
Understanding the statistics of level crossings in stochastic processes is crucial across many scientific disciplines. The traditional Kac-Rice formula gives the mean rate of level…
MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
Cheng Zeng, Harry W. Sullivan, Thomas Egg +8
Molecular crystal structure prediction represents a grand challenge in computational chemistry due to large sizes of constituent molecules and complex intra- and intermolecular int…
Cross-View World Models
Rishabh Sharma, Gijs Hogervorst, Wayne E. Mackey +2
World models enable agents to plan by imagining future states, but existing approaches operate from a single viewpoint, typically egocentric, even when other perspectives would mak…
Open Materials Generation with Inference-Time Reinforcement Learning
Philipp Hoellmer, Stefano Martiniani
Continuous-time generative models for crystalline materials enable inverse materials design by learning to predict stable crystal structures, but incorporating explicit target prop…
Spatial self-organization driven by temporal noise
Satyam Anand, Guanming Zhang, Stefano Martiniani
The counterintuitive emergence of order from noise is a central phenomenon in science, ranging from pattern formation and synchronization to order-by-disorder in frustrated systems…
MolGuidance: Advanced Guidance Strategies for Conditional Molecular Generation with Flow Matching
Jirui Jin, Cheng Zeng, Pawan Prakash +5
Key objectives in conditional molecular generation include ensuring chemical validity, aligning generated molecules with target properties, promoting structural diversity, and enab…
Spatial and Temporal Cluster Tomography of Active Matter
Leone V. Luzzatto, Mathias Casiulis, Stefano Martiniani +1
Critical phase transitions have proven to be a powerful concept to capture the phenomenology of many systems, including deeply non-equilibrium ones like living systems. The study o…
Guided Diffusion for the Discovery of New Superconductors
Pawan Prakash, Jason B. Gibson, Zhongwei Li +13
The inverse design of materials with specific desired properties, such as high-temperature superconductivity, represents a formidable challenge in materials science due to the vast…
All that structure matches does not glitter
Maya M. Martirossyan, Thomas Egg, Philipp Hoellmer +7
Generative models for materials, especially inorganic crystals, hold potential to transform the theoretical prediction of novel compounds and structures. Advancement in this field…
A unifying approach to self-organizing systems interacting via conservation laws
Frank Barrows, Guanming Zhang, Satyam Anand +5
We present a unified framework for embedding and analyzing dynamical systems using generalized projection operators rooted in local conservation laws. By representing physical, bio…
Contrastive Self-Supervised Learning As Neural Manifold Packing
Guanming Zhang, David J. Heeger, Stefano Martiniani
Contrastive self-supervised learning based on point-wise comparisons has been widely studied for vision tasks. In the visual cortex of the brain, neuronal responses to distinct sti…
PropMolFlow: Property-Guided Molecule Generation with Geometry-Complete Flow Matching
Cheng Zeng, Jirui Jin, Connor Ambrose +7
Molecule generation is advancing rapidly in chemical discovery and drug design. Flow matching methods have recently set the state of the art (SOTA) in unconditional molecule genera…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
Open Materials Generation with Stochastic Interpolants
Philipp Hoellmer, Thomas Egg, Maya M. Martirossyan +11
The discovery of new materials is essential for enabling technological advancements. Computational approaches for predicting novel materials must effectively learn the manifold of…
Gyromorphs: a new class of functional disordered materials
Mathias Casiulis, Aaron Shih, Stefano Martiniani
We introduce a new class of functional correlated disordered materials, termed Gyromorphs, which uniquely combine liquid-like translational disorder with quasi-long-range rotationa…
On the design space between molecular mechanics and machine learning force fields
Yuanqing Wang, Kenichiro Takaba, Michael S. Chen +14
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 e…
The Basins of Attraction of Soft Sphere Packings Are Not Fractal
Praharsh Suryadevara, Mathias Casiulis, Stefano Martiniani
The energy landscape picture is a central tool to study many-body systems. In particular, the energy landscapes of glass-forming liquids, jammed packings, constraint satisfaction p…