AMGenC: Generating Charge Balanced Amorphous Materials

• URL: http://arxiv.org/abs/2604.27613v1
• Date: Thu, 30 Apr 2026 09:04:02 GMT
• Title: AMGenC: Generating Charge Balanced Amorphous Materials
• Abstract summary: Amorphous (disordered) materials are solids that have shown great potential in various domains, including energy storage, thermal management, and advanced materials.<n>We propose AMGenC, a new generative inverse design method for amorphous materials that can guarantee the generation of charge balanced samples.
• Score: 10.838098645524248
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Amorphous (disordered) materials are solids that have shown great potential in various domains, including energy storage, thermal management, and advanced materials. Unlike crystalline materials that can be described by unit cells containing a few to hundreds of atoms, amorphous materials require larger simulation cells with at least hundreds to thousands of atoms. To advance the design of amorphous materials with desired properties and facilitate the exploration of their vast design space, generative inverse design has emerged as a promising approach. It aims to directly output materials with properties closely aligned with the desired ones using probabilistic generative models conditioned on desired properties, which can be more resource efficient than the traditional trial-and-error approach. However, due to the inherent stochasticity of probabilistic generative models, when element assignments are unconstrained, a large portion of generated materials may be charge unbalanced, and no existing methods can effectively mitigate this limitation. In this work, we propose AMGenC, a new generative inverse design method for amorphous materials that can guarantee the generation of charge balanced samples, with minimal additional computational overhead and without sacrificing inverse design accuracy. AMGenC achieves this through an element noise that gives the generation process a starting point centered around charge balance, and the combination of a per-step soft projection and a final discrete projection for steering the elements toward exact charge balance throughout the generation. We perform extensive experiments on two amorphous materials datasets. Experimental results provide evidence that AMGenC achieves its design goal.

Related papers

• AMShortcut: An Inference- and Training-Efficient Inverse Design Model for Amorphous Materials [8.97798094422526]
  We introduce AMShortcut, an inference- and training-efficient probabilistic generative model for amorphous materials.<n>AMShortcut enables accurate inference of diverse short- and medium-range structures in amorphous materials with only a few sampling steps.
  arXiv  Detail & Related papers  (2026-03-31T14:39:49Z)
• 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)
• A Generative Diffusion Model for Amorphous Materials [2.007205804138591]
  We present a diffusion model that reliably generates amorphous structures up to 1000 times faster than conventional simulations.<n> Conditional generation allowed sampling large structures at low cooling rates of 10$-2$ K/ps to uncover a ductile-to-brittle transition and mesoporous silica structures.
  arXiv  Detail & Related papers  (2025-07-07T14:08:10Z)
• Predicting ionic conductivity in solids from the machine-learned potential energy landscape [68.25662704255433]
  We propose an approach for the quick and reliable screening of ionic conductors through the analysis of a universal interatomic potential.<n>Eight out of the ten highest-ranked materials are confirmed to be superionic at room temperature in first-principles calculations.<n>Our method achieves a speed-up factor of approximately 50 compared to molecular dynamics driven by a machine-learning potential, and is at least 3,000 times faster compared to first-principles molecular dynamics.
  arXiv  Detail & Related papers  (2024-11-11T09:01:36Z)
• Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks [52.13486402193811]
  New solid-state materials require rapidly exploring the vast space of crystal structures and locating stable regions. Existing methods struggle to explore large material spaces and generate diverse samples with desired properties and requirements. We propose a novel generative model employing a hierarchical exploration strategy to efficiently exploit the symmetry of the materials space to generate crystal structures given desired properties.
  arXiv  Detail & Related papers  (2024-11-06T23:53:34Z)
• 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)
• MatterGen: a generative model for inorganic materials design [20.119976243451216]
  We present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table. Compared to prior generative models structures produced by MatterGen are more than twice as likely to be novel and stable. We demonstrate multi-property materials design capabilities by proposing structures that have both high magnetic density and a chemical composition with low supply-chain risk.
  arXiv  Detail & Related papers  (2023-12-06T18:52:16Z)
• 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)
• Materials Transformers Language Models for Generative Materials Design: a benchmark study [4.047301375093173]
  We train seven modern transformer language models (GPT, GPT-2, GPT-Neo, GPT-J, BLMM, BART, and RoBERTa) using the expanded formulas from material deposited in the ICSD, OQMD, and Materials Projects databases. Six different datasets with/out non-charge-neutral or balanced electronegativity samples are used to benchmark the performances. Experiments showed that the causal language models based materials transformers can generate chemically valid materials compositions with as high as 97.54% to be charge neutral and 91.40% to be electronegativity balanced.
  arXiv  Detail & Related papers  (2022-06-27T18:50:05Z)
• Physics Guided Generative Adversarial Networks for Generations of Crystal Materials with Symmetry Constraints [9.755053639966185]
  We propose a Physics Guided Crystal Generative Model (PGCGM) for new materials generation. By augmenting the base atom sites of materials, our model can generate new materials of 20 space groups. Our method increases the generator's validity by 8 times compared to one of the baselines.
  arXiv  Detail & Related papers  (2022-03-27T17:21:36Z)
• 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.