Hash Chemistry: Minimal Models for Evolutionary Growth of Complexity

• URL: http://arxiv.org/abs/2607.28219v1
• Date: Thu, 30 Jul 2026 13:54:23 GMT
• Title: Hash Chemistry: Minimal Models for Evolutionary Growth of Complexity
• Abstract summary: Hash Chemistry is a family of minimalistic evolutionary models in which a deterministic hash function assigns a scalar score to entities of arbitrary size.<n>Here we review the Hash Chemistry family as a coherent modeling framework and use it to explore how minimal systems can demonstrate multiscale open-ended evolutionary dynamics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hash Chemistry is a family of minimalistic evolutionary models in which a deterministic hash function assigns a scalar score to entities of arbitrary size, opening a combinatorially vast possibility space (a ``cardinality leap''). Since its introduction, the idea has been realized in several settings, from the original spatial formulation to a fast non-spatial variant and then to structural cellular models. Here we review the Hash Chemistry family as a coherent modeling framework and use it to explore how minimal systems can demonstrate the mechanisms behind multiscale open-ended evolutionary dynamics. The most recent model, Structural Cellular Hash Chemistry (SCHC), successfully demonstrated multiscale ecological interaction/adaptation and complexity growth of replicators in a computationally efficient manner. In this study, we first extend SCHC to incorporate spatial locality and dyadicity of competitive interactions among replicating structures. We show this extension substantially enhances SCHC's evolutionary dynamics. Furthermore, we explore SCHC in a significantly larger spatial domain using a GPU-accelerated implementation. We show that the size of the space acts as a control parameter for a stochastic, nucleation-like transition between a compact-replicator regime and a runaway size-dominance regime, and we separate the responsible mechanism into a non-spatial, size-biased sampling feedback and a finite-size spatial effect. Altogether, these results illustrate the rich potential of Hash Chemistry as a minimal, mechanistically transparent testbed for studying open-ended evolution across scales.

Related papers

• Sesame: Structure-Aware Molecular Generation via Spatial Density-Map Conditioning [0.0]
  We present Sesame, a diffusion-based molecular generation model.<n>It supports both de novo generation and fragment-conditioned lead optimization.<n>Sesame is trained on a large corpus of protein-ligand datasets.
  arXiv  Detail & Related papers  (2026-06-22T18:48:10Z)
• Foundation Models for Discovery and Exploration in Chemical Space [57.97784111110166]
  MIST is a family of molecular foundation models trained on large unlabeled datasets.<n>We demonstrate the ability of these models to solve real-world problems across chemical space.
  arXiv  Detail & Related papers  (2025-10-20T17:56:01Z)
• High-Fidelity Scientific Simulation Surrogates via Adaptive Implicit Neural Representations [51.90920900332569]
  Implicit neural representations (INRs) offer a compact and continuous framework for modeling spatially structured data.<n>Recent approaches address this by introducing additional features along rigid geometric structures.<n>We propose a simple yet effective alternative: Feature-Adaptive INR (FA-INR)
  arXiv  Detail & Related papers  (2025-06-07T16:45:17Z)
• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters.<n>Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks.<n>It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• Structural Cellular Hash Chemistry [0.24475591916185496]
  Hash Chemistry is a minimalistic artificial chemistry model of open-ended evolution.<n>It has been extended to non-spatial and cellular versions.<n>We propose an improved version called Structural Cellular Hash Chemistry.
  arXiv  Detail & Related papers  (2024-12-17T10:51:01Z)
• RGFN: Synthesizable Molecular Generation Using GFlowNets [51.33672611338754]
  We propose Reaction-GFlowNet, an extension of the GFlowNet framework that operates directly in the space of chemical reactions. RGFN allows out-of-the-box synthesizability while maintaining comparable quality of generated candidates. We demonstrate the effectiveness of the proposed approach across a range of oracle models, including pretrained proxy models and GPU-accelerated docking.
  arXiv  Detail & Related papers  (2024-06-01T13:11:11Z)
• Non-Spatial Hash Chemistry as a Minimalistic Open-Ended Evolutionary System [0.24475591916185496]
  We propose a non-spatial variant of Hash Chemistry in which spatial proximity of particles are represented explicitly in the form of multisets. Results of numerical simulations showed much more significant growth in both maximal and average sizes of replicating higher-order entities than the original model.
  arXiv  Detail & Related papers  (2024-04-27T23:29:55Z)
• HEroBM: a deep equivariant graph neural network for universal backmapping from coarse-grained to all-atom representations [0.0]
  coarse-grained (CG) techniques have emerged as invaluable tools to sample large-scale systems. They sacrifice atomistic details that might hold significant relevance in deciphering the investigated process. A recommended approach is to identify key CG conformations and process them using backmapping methods, which retrieve atomistic coordinates. We introduce HEroBM, a dynamic and scalable method that employs deep equivariant graph neural networks and a hierarchical approach to achieve high-resolution backmapping.
  arXiv  Detail & Related papers  (2024-04-25T13:54:31Z)
• Improving Molecular Properties Prediction Through Latent Space Fusion [9.912768918657354]
  We present a multi-view approach that combines latent spaces derived from state-of-the-art chemical models. Our approach relies on two pivotal elements: the embeddings derived from MHG-GNN, which represent molecular structures as graphs, and MoLFormer embeddings rooted in chemical language. We demonstrate the superior performance of our proposed multi-view approach compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-20T20:29:32Z)
• Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning [60.02391969049972]
  We introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system.
  arXiv  Detail & Related papers  (2023-06-08T17:12:08Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.