VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials

• URL: http://arxiv.org/abs/2604.27685v1
• Date: Thu, 30 Apr 2026 10:20:58 GMT
• Title: VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials
• Abstract summary: We introduce VibroML, an open-source Python toolkit driven by machine-learned interatomic potentials.<n>VibroML employs an energy-guided genetic algorithm that vastly outperforms traditional soft-mode following.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While machine-learned interatomic potentials (MLIPs) accelerate phonon dispersion calculations, merely identifying dynamical instabilities in computationally predicted materials is insufficient; automated pathways to resolve them are required. We introduce VibroML, an open-source Python toolkit driven by foundational MLIPs that shifts the paradigm from stability verification to automated structural remediation. VibroML employs an energy-guided genetic algorithm that vastly outperforms traditional soft-mode following, efficiently navigating the potential energy surface to uncover diverse, dynamically stable polymorphs. As 0 K harmonic stability does not guarantee macroscopic viability, an automated molecular dynamics workflow evaluates finite-temperature structural retention. VibroML also couples with ProtoCSP, our combinatorial structure prediction engine, to stabilize frustrated crystal topologies via targeted alloying, successfully rescuing functional perovskite networks like Cs$_2$KInI$_6$ and KTaSe$_3$. Demonstrating broader applicability, we mined the Alexandria database -- where ~50% of quaternary and 99.5% of quinary elemental combinations lack any structural entries -- to identify thousands of abandoned, high-symmetry stoichiometries. Deploying ProtoCSP's "cold start" retrieval and VibroML's evolutionary search on a sample, we successfully identified dynamically stable low-symmetry candidates. Through integrated structural remediation, thermal validation, and systematic compositional exploration, VibroML enables a comprehensive deep-screening approach, yielding physically sound structural propositions that far surpass standard high-throughput workflows.

Related papers

• SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis [40.95932731774983]
  SpaCellAgent is an autonomous language model that automates end-to-end paradigm for transcriptomics analysis.<n>SpaCellAgent consistently demonstrates over 40% improvement in analytical efficiency while maintaining expert-aligned performance.
  arXiv  Detail & Related papers  (2026-07-08T14:31:46Z)
• Hybrid Neural Ordinary Differential Equations for Data-Efficient Polymerization Modeling with Incomplete Kinetics [42.58292307702424]
  We propose a hybrid Neural Ordinary Differential Equation framework for data-efficient modeling of free-radical polymerization.<n>The hybrid NODE is evaluated against a discrete-time feedforward neural network and a purely data-driven NODE under sparse data conditions.<n>In a generalization scenario with noisy data and unseen operating conditions, the hybrid NODE achieves an RMSE of 0.013, compared to 0.31 for the data-driven NODE and 0.68 for the discrete-time model.
  arXiv  Detail & Related papers  (2026-06-01T12:10:48Z)
• SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction [46.645872209565574]
  We present a specialized large-scale foundation model for virtual cell perturbation prediction.<n>First, we build a BioNeMo-based training and inference framework that substantially improves data throughput.<n>Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture.<n>Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics.
  arXiv  Detail & Related papers  (2026-03-18T05:51:08Z)
• From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark [67.29937933325849]
  Operando IR Prediction aims to forecast the time-resolved evolution of spectral fingerprints'' from a single static spectrum.<n>OpIRSpec-7K comprises 7,118 high-quality samples across 10 distinct battery systems.<n>ABCC significantly outperforms state-of-the-art static, sequential, and generative baselines.
  arXiv  Detail & Related papers  (2026-02-20T18:58:43Z)
• From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures [12.68400434984463]
  MLIPs fail to reproduce the physical smoothness of the quantum potential energy surface.<n>Existing evaluations, such as microcanonical molecular dynamics, are computationally expensive and primarily probe near-equilibrium states.<n>We introduce the Bond Smoothness Characterization Test (BSCT) to improve evaluation metrics for MLIPs.
  arXiv  Detail & Related papers  (2026-02-04T18:50:10Z)
• Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics [51.85385061275941]
  Molecular dynamics (MD) simulations remain the gold standard for studying protein dynamics.<n>Recent generative models have shown promise in accelerating simulations, yet they struggle with long-horizon generation.<n>We present STAR-MD, a scalable diffusion model that generates physically plausible protein trajectories over micro-scale timescales.
  arXiv  Detail & Related papers  (2026-02-02T14:13:28Z)
• Reframing Generative Models for Physical Systems using Stochastic Interpolants [45.16806809746592]
  Generative models have emerged as powerful surrogates for physical systems, demonstrating increased accuracy, stability, and/or statistical fidelity.<n>Most approaches rely on iteratively denoising a Gaussian, a choice that may not be the most effective for autoregressive prediction tasks in PDEs and dynamical systems such as climate.<n>In this work, we benchmark generative models across diverse physical domains and tasks, and highlight the role of interpolants.
  arXiv  Detail & Related papers  (2025-09-30T14:02:00Z)
• Automated Machine Learning Pipeline for Training and Analysis Using Large Language Models [0.0]
  We introduce an Automated Machine Learning Pipeline (AMLP) that unifies the entire workflow from dataset creation to model validation.<n>AMLP employs large-language-model agents to assist with electronic-structure code selection, input preparation, and output conversion.<n>It is validated on acridine polymorphs, where, with a straightforward fine-tuning of a foundation model, mean absolute errors of 1.7 meV/atom in energies and 7.0 meV/AA in forces are achieved.
  arXiv  Detail & Related papers  (2025-09-25T22:05:20Z)
• TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing [50.95799256262098]
  We introduceHyper-VQC, a novel tensor-train (TT)-guided hypernetwork framework for quantum machine learning.<n>Our framework delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.<n>These results positionHyper-VQC as a scalable and noise-resilient framework for advancing practical quantum machine learning on near-term devices.
  arXiv  Detail & Related papers  (2025-08-01T23:37:55Z)
• From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields [5.013279299982324]
  We propose a transformer architecture called SO3krates that combines sparse equivariant representations with a self-attention mechanism. SO3krates achieves a unique combination of accuracy, stability, and speed that enables insightful analysis of quantum properties of matter on extended time and system size scales.
  arXiv  Detail & Related papers  (2023-09-21T09:22:05Z)
• Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks [0.9187159782788578]
  MOFs hold promise for engineering challenges ranging from gas separations to stability. To overcome limitation, we extract thousands of published reports of the key aspects of MOF stability necessary for their practical application. We use natural language processing and automated image analysis to obtain over 2,000 solvent-removal measures and 3,000 thermal temperatures.
  arXiv  Detail & Related papers  (2021-06-24T21:35:26Z)
• Automated discovery of a robust interatomic potential for aluminum [4.6028828826414925]
  Machine learning (ML) based potentials aim for faithful emulation of quantum mechanics (QM) calculations at drastically reduced computational cost. We present a highly automated approach to dataset construction using the principles of active learning (AL) We demonstrate this approach by building an ML potential for aluminum (ANI-Al) To demonstrate transferability, we perform a 1.3M atom shock simulation, and show that ANI-Al predictions agree very well with DFT calculations on local atomic environments sampled from the nonequilibrium dynamics.
  arXiv  Detail & Related papers  (2020-03-10T19:06:32Z)

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.