Atomistic structure search using local surrogate mode

• URL: http://arxiv.org/abs/2208.09273v1
• Date: Fri, 19 Aug 2022 11:40:05 GMT
• Title: Atomistic structure search using local surrogate mode
• Abstract summary: We describe a local surrogate model for use in conjunction with global structure search methods. The model is implemented in the Atomistic Global Optimization X framework. It is shown to be robust for a wide range of atomistic system including molecules, nano-particles, surface supported clusters and surface thin films.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We describe a local surrogate model for use in conjunction with global structure search methods. The model follows the Gaussian approximation potential (GAP) formalism and is based on a the smooth overlap of atomic positions descriptor with sparsification in terms of a reduced number of local environments using mini-batch $k$-means. The model is implemented in the Atomistic Global Optimization X framework and used as a partial replacement of the local relaxations in basin hopping structure search. The approach is shown to be robust for a wide range of atomistic system including molecules, nano-particles, surface supported clusters and surface thin films. The benefits in a structure search context of a local surrogate model are demonstrated. This includes the ability to transfer learning from smaller systems as well as the possibility to perform concurrent multi-stoichiometry searches.

Related papers

• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback [56.69850045068714]
  We introduce textitInfluence-Guided Regression (IGSR), a method that frames equation discovery as an iterative two-step process.<n>We demonstrate IGSRs effectiveness on a diverse suite of benchmarks, including LLM-SRBench, pharmacological PKPD models, an epidemiological simulation, and real-world genomic data.
  arXiv  Detail & Related papers  (2026-05-27T23:48:01Z)
• Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning [8.150979148487787]
  We introduce Meta-LegNet, a graph learning framework that learns transferable representations of local environments across diverse catalyst--adsorbate systems.<n>Our results suggest that learning transferable adsorbing environments provides an accurate, interpretable, and practical route for accelerating catalyst screening.
  arXiv  Detail & Related papers  (2026-05-03T10:55:16Z)
• A general optimization framework for mapping local transition-state networks [0.0]
  Local transition-state networks reveal the barrier architecture that explains observed behaviour.<n>We present a general optimization framework that systematically expands local coverage by coupling a multi-objective explorer with a bilayer minimum-mode kernel.
  arXiv  Detail & Related papers  (2025-09-30T13:54:10Z)
• Scalable Context-Preserving Model-Aware Deep Clustering for Hyperspectral Images [51.95768218975529]
  Subspace clustering has become widely adopted for the unsupervised analysis of hyperspectral images (HSIs)<n>Recent model-aware deep subspace clustering methods often use a two-stage framework, involving the calculation of a self-representation matrix with complexity of O(n2), followed by spectral clustering.<n>We propose a scalable, context-preserving deep clustering method based on basis representation, which jointly captures local and non-local structures for efficient HSI clustering.
  arXiv  Detail & Related papers  (2025-06-12T16:43:09Z)
• Localized Diffusion Models for High Dimensional Distributions Generation [1.677718351174347]
  We study the importance of better understanding and exploiting low-dimensional structure in the target distribution.<n>Under locality structure, the score function is effectively low-dimensional, so that it can be estimated by a localized neural network with significantly reduced sample complexity.<n>We prove that such localization enables diffusion models to circumvent the curse of dimensionality (CoD)
  arXiv  Detail & Related papers  (2025-05-07T13:51:50Z)
• Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data [0.0]
  Coarse-grained (CG) models provide an effective route to reducing the complexity of molecular simulations.<n>We introduce a fully data-free, generative framework for CG that directly targets the all-atom Boltzmann distribution.<n>We show that the method captures all relevant modes of the Boltzmann distribution, reconstructs atomic configurations, and automatically learns physically meaningful CG representations.
  arXiv  Detail & Related papers  (2025-04-29T17:05:27Z)
• Generating Generalised Ground-State Ansatzes from Few-Body Examples [0.0]
  We introduce a method that generates ground-state ansatzes for quantum many-body systems. The ansatzes are analytically tractable and accurate over wide parameter regimes. We demonstrate this method on the Lipkin-Meshkov-Glick model (LMG) and the quantum transverse-field Ising model (TFIM)
  arXiv  Detail & Related papers  (2025-03-01T13:52:57Z)
• Modeling All Response Surfaces in One for Conditional Search Spaces [69.90317997694218]
  This paper proposes a novel approach to model the response surfaces of all subspaces in one. We introduce an attention-based deep feature extractor, capable of projecting configurations with different structures from various subspaces into a unified feature space.
  arXiv  Detail & Related papers  (2025-01-08T03:56:06Z)
• Computing Low-Entropy Couplings for Large-Support Distributions [53.00113867130712]
  Minimum-entropy coupling has applications in areas such as causality and steganography. Existing algorithms are either computationally intractable for large-support distributions or limited to specific distribution types. This work addresses these limitations by unifying a prior family of iterative MEC approaches into a generalized partition-based formalism.
  arXiv  Detail & Related papers  (2024-05-29T21:54:51Z)
• Fleet of Agents: Coordinated Problem Solving with Large Language Models using Genetic Particle Filtering [10.167121757937062]
  Fleet of Agents (FoA) is a framework utilizing large language models as agents to navigate through dynamic tree searches. FoA spawns a multitude of agents, each exploring autonomously, followed by a selection phase. We experimentally validate FoA using two benchmark tasks, "Game of 24 and "Mini-Crosswords"
  arXiv  Detail & Related papers  (2024-05-07T09:36:23Z)
• Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation [84.0621253654014]
  We propose a framework, called BALLET, which adaptively filters for a high-confidence region of interest. We show theoretically that BALLET can efficiently shrink the search space, and can exhibit a tighter regret bound than standard BO.
  arXiv  Detail & Related papers  (2023-07-25T09:45:47Z)
• A framework for efficient ab initio electronic structure with Gaussian Process States [0.0]
  We present a framework for the efficient simulation of realistic fermionic systems with modern machine learning inspired representations of quantum many-body states. We show competitive accuracy for systems with up to 64 electrons, including a simplified (yet fully ab initio) model of the Mott transition in three-dimensional hydrogen.
  arXiv  Detail & Related papers  (2023-02-02T13:40:38Z)
• Local Sample-weighted Multiple Kernel Clustering with Consensus Discriminative Graph [73.68184322526338]
  Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. This paper proposes a novel local sample-weighted multiple kernel clustering model. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algo-rithms.
  arXiv  Detail & Related papers  (2022-07-05T05:00:38Z)
• Low-Rank Constraints for Fast Inference in Structured Models [110.38427965904266]
  This work demonstrates a simple approach to reduce the computational and memory complexity of a large class of structured models. Experiments with neural parameterized structured models for language modeling, polyphonic music modeling, unsupervised grammar induction, and video modeling show that our approach matches the accuracy of standard models at large state spaces.
  arXiv  Detail & Related papers  (2022-01-08T00:47:50Z)
• Global Aggregation then Local Distribution for Scene Parsing [99.1095068574454]
  We show that our approach can be modularized as an end-to-end trainable block and easily plugged into existing semantic segmentation networks. Our approach allows us to build new state of the art on major semantic segmentation benchmarks including Cityscapes, ADE20K, Pascal Context, Camvid and COCO-stuff.
  arXiv  Detail & Related papers  (2021-07-28T03:46:57Z)
• Clustered Federated Learning via Generalized Total Variation Minimization [83.26141667853057]
  We study optimization methods to train local (or personalized) models for local datasets with a decentralized network structure. Our main conceptual contribution is to formulate federated learning as total variation minimization (GTV) Our main algorithmic contribution is a fully decentralized federated learning algorithm.
  arXiv  Detail & Related papers  (2021-05-26T18:07:19Z)
• Locally induced Gaussian processes for large-scale simulation experiments [0.0]
  We show how placement of inducing points and their multitude can be thwarted by pathologies. Our proposed methodology hybridizes global inducing point and data subset-based local GP approximation. We show that local inducing points extend their global and data-subset component parts on the accuracy--computational efficiency frontier.
  arXiv  Detail & Related papers  (2020-08-28T21:37:46Z)
• Max-Affine Spline Insights into Deep Generative Networks [8.579613053834342]
  We connect a large class of Generative Deep Networks (GDNs) with spline operators in order to derive their properties, limitations, and new opportunities. By characterizing the latent space partition, dimension and angularity of the generated manifold, we relate the manifold dimension and approximation error to the sample size. We derive the output probability density mapped onto the generated manifold in terms of the latent space density, which enables the computation of key statistics such as its Shannon entropy.
  arXiv  Detail & Related papers  (2020-02-26T00:20:02Z)

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.