Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials

• URL: http://arxiv.org/abs/2503.15432v1
• Date: Wed, 19 Mar 2025 17:14:02 GMT
• Title: Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials
• Authors: Johnathan D. Georgaras, Akash Ramdas, Chung Hsuan Shan, Elena Halsted, Berwyn, Tianshu Li, Felipe H. da Jornada,
• Abstract summary: Twisted layered van-der-Waals materials often exhibit unique electronic and optical properties absent in their non-twisted counterparts.<n>Here, we introduce a split machine-learned interatomic potential and dataset curation approach that separates intralayer and interlayer interactions.<n>Our approach integrates seamlessly with various intralayer and interlayer interaction models, enabling computationally tractable relaxation of moir'e materials.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Twisted layered van-der-Waals materials often exhibit unique electronic and optical properties absent in their non-twisted counterparts. Unfortunately, predicting such properties is hindered by the difficulty in determining the atomic structure in materials displaying large moir\'e domains. Here, we introduce a split machine-learned interatomic potential and dataset curation approach that separates intralayer and interlayer interactions and significantly improves model accuracy -- with a tenfold increase in energy and force prediction accuracy relative to conventional models. We further demonstrate that traditional MLIP validation metrics -- force and energy errors -- are inadequate for moir\'e structures and develop a more holistic, physically-motivated metric based on the distribution of stacking configurations. This metric effectively compares the entirety of large-scale moir\'e domains between two structures instead of relying on conventional measures evaluated on smaller commensurate cells. Finally, we establish that one-dimensional instead of two-dimensional moir\'e structures can serve as efficient surrogate systems for validating MLIPs, allowing for a practical model validation protocol against explicit DFT calculations. Applying our framework to HfS2/GaS bilayers reveals that accurate structural predictions directly translate into reliable electronic properties. Our model-agnostic approach integrates seamlessly with various intralayer and interlayer interaction models, enabling computationally tractable relaxation of moir\'e materials, from bilayer to complex multilayers, with rigorously validated accuracy.

Related papers

• Machine Learning for Improved Density Functional Theory Thermodynamics [0.0]
  We present a machine learning (ML) approach to systematically correct intrinsic energy resolution errors in density functional theory calculations. A neural network model has been trained to predict the discrepancy between DFT-calculated and experimentally measured enthalpies for binary and ternary alloys and compounds. We illustrate the effectiveness of this method by applying it to the Al-Ni-Pd and Al-Ni-Ti systems, which are of interest for high-temperature applications in aerospace and protective coatings.
  arXiv  Detail & Related papers  (2025-03-07T15:46:30Z)
• Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation [73.81105275628751]
  Finding all structures compatible with reflectometry data is computationally prohibitive for standard algorithms. We address this lack of reliability with a probabilistic deep learning method that identifies all realistic structures in seconds. Our method, Prior-Amortized Neural Posterior Estimation (PANPE), combines simulation-based inference with novel adaptive priors.
  arXiv  Detail & Related papers  (2024-07-26T10:29:16Z)
• Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials [0.5812062802134551]
  We show the results of adjusting and fine-tuning the pretrained EquiformerV2 model from the Open Catalyst Project. By applying an energy filter based on the local environment of the binding site the zero-shot inference is markedly improved. It is also found that EquiformerV2, assuming the role of general machine learning potential, is able to inform a smaller, more focused direct inference model.
  arXiv  Detail & Related papers  (2024-03-14T18:59:54Z)
• Electronic excited states from physically-constrained machine learning [0.0]
  We present an integrated modeling approach, in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those that it is trained on.
  arXiv  Detail & Related papers  (2023-11-01T20:49:59Z)
• The Role of Reference Points in Machine-Learned Atomistic Simulation Models [0.0]
  Chemical Environment Modeling Theory (CEMT) is designed to overcome the limitations inherent in traditional atom-centered Machine Learning Force Field (MLFF) models. It allows the leveraging of spatially-resolved energy densities and charge densities from FE-DFT calculations.
  arXiv  Detail & Related papers  (2023-10-28T01:02:14Z)
• Electronic Structure Prediction of Multi-million Atom Systems Through Uncertainty Quantification Enabled Transfer Learning [5.4875371069660925]
  Ground state electron density -- obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations -- contains a wealth of material information. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data.
  arXiv  Detail & Related papers  (2023-08-24T21:41:29Z)
• DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion [66.21290235237808]
  We introduce an energy constrained diffusion model which encodes a batch of instances from a dataset into evolutionary states. We provide rigorous theory that implies closed-form optimal estimates for the pairwise diffusion strength among arbitrary instance pairs. Experiments highlight the wide applicability of our model as a general-purpose encoder backbone with superior performance in various tasks.
  arXiv  Detail & Related papers  (2023-01-23T15:18:54Z)
• Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction [61.9212585617803]
  Graph neural networks (GNNs) have drawn more and more attention from material scientists. We develop a multi-task (MT) architecture based on DimeNet++ and mixture density networks to improve the performance of such task.
  arXiv  Detail & Related papers  (2022-09-15T13:52:15Z)
• Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models [68.9288651177564]
  We present a novel MoE architecture based on matrix product operators (MPO) from quantum many-body physics. With the decomposed MPO structure, we can reduce the parameters of the original MoE architecture. Experiments on the three well-known downstream natural language datasets based on GPT2 show improved performance and efficiency in increasing model capacity.
  arXiv  Detail & Related papers  (2022-03-02T13:44:49Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• BIGDML: Towards Exact Machine Learning Force Fields for Materials [55.944221055171276]
  Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 atoms.
  arXiv  Detail & Related papers  (2021-06-08T10:14:57Z)

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.