Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation

• URL: http://arxiv.org/abs/2607.28776v1
• Date: Thu, 30 Jul 2026 18:56:34 GMT
• Title: Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation
• Abstract summary: We showcase the power of atom-averaged features from pretrained Machine-Learning Interatomic Potentials (MLIPs)<n>We first introduce a distance measure that assesses the output of material generative models.<n>We show that coarse MACE features can be used as guidance for a material generative model.
• Score: 2.6310284833506414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative machine learning is increasingly used for inorganic crystal structure generation. Most models and the corresponding evaluation approaches rely on simple forms of crystal structure representation. In this paper, we showcase the power of atom-averaged features from pretrained Machine-Learning Interatomic Potentials (MLIPs), such as MACE, for such tasks. We first introduce a distance measure that assesses the output of material generative models by capturing both quality and novelty in a single distribution-based evaluation framework. In particular, we introduce the Coarse-Fine Transport Distance (CFTD) using two different featurizers, where the quality component is based on coarse MACE features. We showcase CFTD's versatility in capturing crystal-structure quality while also detecting memorization, and compare it with the recently introduced continuous SUN metrics. We further show that coarse MACE features can be used as guidance for a material generative model.

Related papers

• Packora: Systematic Design for Generative Molecular Crystal Structure Prediction [29.599535841852727]
  We present Packora, a flow-based generative model for molecular crystal structure prediction (CSP)<n>Packora supports multi-component and organometallic crystals and can condition on any subset of molecular conformers, stereochemical labels, and space-group information within a single model.<n>We evaluate generation and ranking separately, using generation to isolate generator quality and ranking to measure end-to-end performance under a common relaxation and ranking pipeline.
  arXiv  Detail & Related papers  (2026-08-27T11:01:18Z)
• An Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classification [51.004475033276215]
  We build two MPC datasets and propose an enhanced geometric-spectral feature learning framework based on attentions for airborne MPC classification.<n>A key component in our model is a two-stream feature fusion method with attention mechanisms, which enhances the representation capability of spatial-spectral features.<n> Experimental results on two airborne MPC datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-06-08T07:18:38Z)
• Zatom-1: A Multimodal Flow Foundation Model for 3D Molecules and Materials [51.342983349686556]
  General-purpose 3D chemical modeling encompasses molecules and materials, requiring both generative and predictive capabilities.<n>We introduce Zatom-1, the first end-to-end, fully open-source foundation model that unifies generative and predictive learning of 3D molecules and materials.
  arXiv  Detail & Related papers  (2026-02-24T20:52:39Z)
• Transport Novelty Distance: A Distributional Metric for Evaluating Material Generative Models [2.5779675962411654]
  We introduce the Transport Novelty Distance (TNovD) to judge generative models used for materials discovery jointly by the quality and novelty of the generated materials.<n>Based on ideas from Optimal Transport theory, TNovD uses a coupling between the features of the training and generated sets, which is refined into a quality and memorization regime by a threshold.<n>We evaluate our proposed metric on typical toy experiments relevant for crystal structure prediction, including memorization, noise injection and lattice deformations.
  arXiv  Detail & Related papers  (2025-12-10T10:38:58Z)
• 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)
• Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework [2.172419551358714]
  MultiCrystalSpectrumSet (MCS-Set) is a curated framework that expands materials datasets by integrating atomic structures with 2D projections and structured textual annotations.<n>MCS-Set enables two key tasks: (1) multimodal property and summary prediction, and (2) constrained crystal generation with partial cluster supervision.
  arXiv  Detail & Related papers  (2025-05-30T23:18:42Z)
• Open Materials Generation with Stochastic Interpolants [14.939468363546384]
  Open Materials Generation (OMatG) is a unifying framework for the generative design and discovery of crystalline materials.<n>OMatG employs inorganic interpolants to bridge an arbitrary base distribution to the target distribution of inorganic crystals.<n>We benchmark OMatG's performance on two tasks: Crystal Structure Prediction and 'de novo' generation.
  arXiv  Detail & Related papers  (2025-02-04T18:56:47Z)
• Fine-Tuned Language Models Generate Stable Inorganic Materials as Text [53.81190146434045]
  Fine-tuning large language models on text-encoded atomistic data is simple to implement yet reliable.<n>We show that our strongest model can generate materials predicted to be metastable at about twice the rate of CDVAE.<n>Because of text prompting's inherent flexibility, our models can simultaneously be used for unconditional generation of stable material.
  arXiv  Detail & Related papers  (2024-02-06T20:35:28Z)
• Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models [6.97950396242977]
  We introduce a new framework based on the diffusion model to predict 3D structures of disordered materials from a target property. We show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination.
  arXiv  Detail & Related papers  (2023-12-09T05:40:10Z)
• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning [82.67551367327634]
  We develop a new interpretable, multi-resolution machine learning framework for finding patterns in the unit-cells of materials. Specifically, we propose two new interpretable representations of metamaterials, called shape-frequency features and unit-cell templates.
  arXiv  Detail & Related papers  (2021-11-10T21:19:02Z)

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.