Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design

• URL: http://arxiv.org/abs/2606.28578v1
• Date: Fri, 26 Jun 2026 20:10:01 GMT
• Title: Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design
• Abstract summary: We show that a cheap probabilistic surrogate can triage the generator's output, and what such a surrogate must do well.<n>A cross-factorial benchmark of surrogate performance spanning mechanical, electronic, and vibrational properties identifies pretrained ORB embeddings with a Gaussian process as the most reliable combination.<n>The complete pipeline is released as open-source software.
• Score: 0.7500610196128554
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Closed-loop materials discovery iterates between proposing candidate structures and evaluating their properties, and property evaluation dominates the cost. In the generative variant, a learned prior proposes candidate crystals and a property oracle scores them; we ask whether a cheap probabilistic surrogate can triage the generator's output, and what such a surrogate must do well. Across three architecturally distinct pretrained diffusion priors (MatterGen, CrystalFlow, ADiT) and two targets (room-temperature heat capacity and bulk modulus), we insert a Gaussian process acquisition gate between structure generation and the oracle in an RL-steered generative workflow. The gate matches or exceeds ungated fine-tuning of the generative model while capping oracle calls at a fixed per-cycle budget. Budget-matched ablations isolate the mechanism. At an identical four-call budget, ranking-based selection outperforms arbitrary selection, confirming that the gain comes from the surrogate's choice; the gate comes within $\sim$9\% of exhaustive oracle spending at roughly one-fifth of the calls. A density-functional-theory check of the bulk-modulus discoveries confirms the learned oracle to within 2.5\% on average and the surrogate's ranking of the generated structures at Spearman $ρ= 0.94$. A cross-factorial benchmark of surrogate performance spanning mechanical, electronic, and vibrational properties identifies pretrained ORB embeddings with a Gaussian process as the most reliable combination, which we adopt as the building blocks of the proposed workflow. The complete pipeline is released as open-source software.

Related papers

• Agentic Stage-One Stellarator Optimization: Autonomous Multi-Objective Search for Finite-Beta Equilibria [0.0]
  We present a proof of concept for emphagentic stage-one optimization.<n>Agentic outer-loop control can sustain finite-beta, multi-objective search and turn repeated optimization into a scalable source of improved equilibria.
  arXiv  Detail & Related papers  (2026-08-02T16:08:52Z)
• Oracle-Budgeted Molecular Optimization with Short-Term Graph Memory [13.101703587509641]
  We introduce short-term graph memory, a plug-in module that preserves the generator architecture and native update rule.<n>The module maintains an online graph neural surrogate that pre-screens each round's candidate pool.<n>It improves the mean top-10 score at no extra oracle cost and never falls behind the base on any oracle.
  arXiv  Detail & Related papers  (2026-07-30T16:13:02Z)
• CADAQUES: A Cost-Aware Dual Architecture for Query-Efficient Autonomous Discovery [0.0]
  CADAQUES is a first-class primitive of the discovery loop built on one architectural principle.<n>It charges both evaluations and decisions against a common vector-valued budget spanning wall time, CPU hours, monetary cost, and language model tokens.<n>We evaluate the architecture by locating the critical temperature of the two-dimensional Ising model from noisy finite-size estimates.
  arXiv  Detail & Related papers  (2026-07-17T17:04:20Z)
• DaV-Gen: End-to-End Generative Retrieval via Draft-and-Verify [4.546259713278263]
  textbfDaV-Gen is a novel unified solution designed to fundamentally the paradigm for both search and recommendation.<n>During training, the model is concurrently optimized for both candidate drafting and fine-grained verification.<n>This is achieved by a composite loss function that jointly trains the model on two distinct but related objectives.
  arXiv  Detail & Related papers  (2026-07-09T11:22:50Z)
• FlowBank: Query-Adaptive Agentic Workflows Optimization through Precompute-and-Reuse [58.80348559239392]
  Large Language Model (LLM)-based multi-agent systems are increasingly powerful.<n>Task-level methods spend substantial offline compute yet deploy only a single workflow.<n> query-level methods synthesize a new workflow per query at substantial cost.<n>We present FlowBank, a framework for portfolio-based agentic workflow optimization.
  arXiv  Detail & Related papers  (2026-06-09T17:58:21Z)
• INFUSER: Influence-Guided Self-Evolution Improves Reasoning [54.101135873140066]
  We introduce an iterative co-training framework with two co-evolving roles.<n>A solver is trained with standard correctness rewards against the generator-provided answers.<n>An 8B INF co-evolving generator outperforms a frozen 32B thinking generator on math and coding.
  arXiv  Detail & Related papers  (2026-06-08T05:40:36Z)
• Civilizational Metamaterials: Engineering Coordination Under Capability Gradients and Structural Turbulence [0.0]
  We argue that governance must transition from a normative discipline to an engineering discipline.<n>We develop a formal framework, inspired by the physics of metamaterials, to make this transition quantitative and testable.
  arXiv  Detail & Related papers  (2026-05-29T18:10:00Z)
• Bayesian-guided inverse design of hyperelastic microstructures: Application to stochastic metamaterials [0.34376560669160394]
  Our aim is to identify a structure that achieves a target macroscopic stress response.<n>For each candidate design, the response is obtained from a high-fidelity oracle.<n>We propose a Bayesian-guided inverse design framework.
  arXiv  Detail & Related papers  (2026-03-16T21:09:57Z)
• $V_1$: Unifying Generation and Self-Verification for Parallel Reasoners [69.66089681814013]
  $V_$ is a framework that unifies generation and verification through efficient pairwise ranking.<n>$V_$-Infer improves Pass@1 by up to $10%$ over pointwise verification.<n>$V_$-PairRL achieves $7$--$9%$ test-time scaling gains over standard RL and pointwise joint training.
  arXiv  Detail & Related papers  (2026-03-04T17:22:16Z)
• Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem [90.17610617854247]
  We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimize the production pipeline for agentic model.<n>ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering.<n>We release ROME, an open-source agent grounded by ALE and trained on over one million trajectories.
  arXiv  Detail & Related papers  (2025-12-31T14:03:39Z)
• Multiple-play Stochastic Bandits with Prioritized Arm Capacity Sharing [52.124267908936396]
  The model is composed of $M$ arms and $K$ plays.<n>Each arm has a number of capacities, and each unit of capacity is associated with a reward function.<n>When multiple plays compete for the arm capacity, the arm capacity is allocated in a larger priority weight first manner.
  arXiv  Detail & Related papers  (2025-12-25T11:19:09Z)
• InfiGFusion: Graph-on-Logits Distillation via Efficient Gromov-Wasserstein for Model Fusion [36.27704594180795]
  InfiGFusion is a structure-aware fusion framework with a novel textitGraph-on-Logits Distillation (GLD) loss.<n>We show that GLD consistently improves fusion quality and stability.<n>It shows particular strength in complex reasoning tasks, with +35.6 improvement on Multistep Arithmetic and +37.06 on Causal Judgement over SFT.
  arXiv  Detail & Related papers  (2025-05-20T03:55:35Z)
• Minimax and Communication-Efficient Distributed Best Subset Selection with Oracle Property [0.358439716487063]
  The explosion of large-scale data has outstripped the processing capabilities of single-machine systems. Traditional approaches to distributed inference often struggle with achieving true sparsity in high-dimensional datasets. We propose a novel, two-stage, distributed best subset selection algorithm to address these issues.
  arXiv  Detail & Related papers  (2024-08-30T13:22:08Z)
• Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process [8.32027826756131]
  The proposed framework is demonstrated and analyzed on three engineering case studies. It provides improved predictive accuracy over a single source model and transformed but source unaware model.
  arXiv  Detail & Related papers  (2024-07-15T22:27:04Z)
• Refined Mechanism Design for Approximately Structured Priors via Active Regression [50.71772232237571]
  We consider the problem of a revenue-maximizing seller with a large number of items for sale to $n$ strategic bidders. It is well-known that optimal and even approximately-optimal mechanisms for this setting are notoriously difficult to characterize or compute.
  arXiv  Detail & Related papers  (2023-10-11T20:34:17Z)
• Understanding Interlocking Dynamics of Cooperative Rationalization [90.6863969334526]
  Selective rationalization explains the prediction of complex neural networks by finding a small subset of the input that is sufficient to predict the neural model output. We reveal a major problem with such cooperative rationalization paradigm -- model interlocking. We propose a new rationalization framework, called A2R, which introduces a third component into the architecture, a predictor driven by soft attention as opposed to selection.
  arXiv  Detail & Related papers  (2021-10-26T17:39:18Z)

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.