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Generic coarse-grained models are designed such that they are (i) simple and (ii) computationally efficient. They do not aim at representing particular materials, but classes of materials, hence they can offer insight into universal…

Soft Condensed Matter · Physics 2009-02-25 Friederike Schmid

We show that neural networks trained by evolutionary reinforcement learning can enact efficient molecular self-assembly protocols. Presented with molecular simulation trajectories, networks learn to change temperature and chemical potential…

Statistical Mechanics · Physics 2020-06-01 Stephen Whitelam , Isaac Tamblyn

The fabrication of nanomaterials involves self-ordering processes of functional molecules on inorganic surfaces. To obtain specific molecular arrangements, a common strategy is to equip molecules with functional groups. However, focusing on…

Mesoscale and Nanoscale Physics · Physics 2021-04-28 Andreas Jeindl , Jari Domke , Lukas Hörmann , Falko Sojka , Roman Forker , Torsten Fritz , Oliver T. Hofmann

Inference in expressive probabilistic models is generally intractable, which makes them difficult to learn and limits their applicability. Sum-product networks are a class of deep models where, surprisingly, inference remains tractable even…

Machine Learning · Computer Science 2016-11-14 Abram L. Friesen , Pedro Domingos

Turing presented a general representation scheme by which to achieve artificial intelligence - unorganised machines. Significantly, these were a form of discrete dynamical system and yet such representations remain relatively unexplored.…

Emerging Technologies · Computer Science 2012-12-07 Larry Bull , Julian Holley , Ben De Lacy Costello , Andrew Adamatzky

Self-assembly of complex and functional materials remains a grand challenge in soft material science. Efficient assembly depends on a delicate balance between thermodynamic and kinetic effects, requiring fine-tuning affinities and…

Soft Condensed Matter · Physics 2024-11-14 Wei-Shao Wei , Anthony Trubiano , Christian Sigl , Stefan Paquay , Hendrik Dietz , Michael F. Hagan , Seth Fraden

The process of protein synthesis in biological systems resembles a one dimensional driven lattice gas in which the particles have spatial extent, covering more than one lattice site. We expand the well studied Totally Asymmetric Exclusion…

Statistical Mechanics · Physics 2009-11-10 Leah B. Shaw , R. K. P. Zia , Kelvin H. Lee

Inspired by the quest for shape-shifting structures in a range of applications, we show how to create morphable structural materials using a neutrally stable unit cell as a building block. This unit cell is a self-stressed hinged structure…

Soft Condensed Matter · Physics 2021-10-18 Gaurav Chaudhary , S Ganga Prasath , Edward Soucy , L Mahadevan

Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are usually uncomputable, incompatible with theories of biological…

We introduce the Neural Field Turing Machine (NFTM), a differentiable architecture that unifies symbolic computation, physical simulation, and perceptual inference within continuous spatial fields. NFTM combines a neural controller,…

Neural and Evolutionary Computing · Computer Science 2025-09-04 Akash Malhotra , Nacéra Seghouani

The theory of spin models intersects with condensed matter physics, complex systems, graph theory, combinatorial optimization, computational complexity and neural networks. Many ensuing applications rely on the fact that complicated spin…

Mathematical Physics · Physics 2024-08-02 Tobias Reinhart , Benjamin Engel , Gemma De les Coves

We give a complete classification of the infinite dimensional tilting modules over a tame hereditary algebra R. We start our investigations by considering tilting modules of the form T=R_U\oplus R_U /R where U is a union of tubes, and R_U…

Representation Theory · Mathematics 2011-12-06 Lidia Angeleri Hügel , Javier Sánchez

Let $A$ be a hereditary algebra over an algebraically closed field $k$ and $A^{(m)}$ be the $m$-replicated algebra of $A$. Given an $A^{(m)}$-module $T$, we denote by $\delta (T)$ the number of non isomorphic indecomposable summands of $T$.…

Representation Theory · Mathematics 2013-01-24 Shunhua Zhang

Let $R$ be an algebra over a ring $\Bbbk$, $T$ an $R$-algebra, $M$ a finitely generated projective $R$-module, and $N$ a $T$-module. Let $G$ be a linearly reductive group scheme over $\Bbbk$ equipped with a representation…

We study tilings of the plane that combine strong properties of different nature: combinatorial and algorithmic. We prove existence of a tile set that accepts only quasiperiodic and non-recursive tilings. Our construction is based on the…

Discrete Mathematics · Computer Science 2015-06-15 Bruno Durand , Andrei Romashchenko

Filter synthesis is an inverse problem that is traditionally approached rationally by considering spatially disjoint resonators, approximating them as lumped elements, and engineering the coupling of selected pairs. This approach strongly…

Applied Physics · Physics 2024-07-09 Fabian T. Faul , Laurent Cronier , Ali Alhulaymi , A. Douglas Stone , Philipp del Hougne

Molecular self-assembly is a well-known technique to create highly functional nanostructures on surfaces. Self-assembly on two-dimensional materials is a developing field and has already resulted in the discovery of several rich and…

Mesoscale and Nanoscale Physics · Physics 2017-09-04 Avijit Kumar , Kaustuv Banerjee , Peter Liljeroth

We develop some basic results about full amalgamation classes with intrinsic trascendentals. These classes have generics whose models may have finite subsets whose intrinsic closure is not contained in its algebraic closure. We will show…

Logic · Mathematics 2015-12-15 Justin Brody

An integral self-affine tile is the solution of a set equation $\mathbf{A} \mathcal{T} = \bigcup_{d \in \mathcal{D}} (\mathcal{T} + d)$, where $\mathbf{A}$ is an $n \times n$ integer matrix and $\mathcal{D}$ is a finite subset of…

Number Theory · Mathematics 2013-09-02 Wolfgang Steiner , Jörg Thuswaldner

Universality is a key hypothesis in mechanistic interpretability -- that different models learn similar features and circuits when trained on similar tasks. In this work, we study the universality hypothesis by examining how small neural…

Machine Learning · Computer Science 2023-05-26 Bilal Chughtai , Lawrence Chan , Neel Nanda
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