EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields

• URL: http://arxiv.org/abs/2403.17507v1
• Date: Tue, 26 Mar 2024 09:09:40 GMT
• Title: EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields
• Authors: Bangchen Yin, Yue Yin, Yuda W. Tang, Hai Xiao,
• Abstract summary: Machine learning force fields (MLFFs) have emerged as a promising approach to bridge the accuracy of quantum mechanical methods. We propose a novel ensemble learning framework, EL-MLFFs, which leverages the stacking method to integrate predictions from diverse MLFFs. We evaluate our approach on two distinct datasets: methane molecules and methanol adsorbed on a Cu(100) surface.
• Score: 1.8367772188990783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning force fields (MLFFs) have emerged as a promising approach to bridge the accuracy of quantum mechanical methods and the efficiency of classical force fields. However, the abundance of MLFF models and the challenge of accurately predicting atomic forces pose significant obstacles in their practical application. In this paper, we propose a novel ensemble learning framework, EL-MLFFs, which leverages the stacking method to integrate predictions from diverse MLFFs and enhance force prediction accuracy. By constructing a graph representation of molecular structures and employing a graph neural network (GNN) as the meta-model, EL-MLFFs effectively captures atomic interactions and refines force predictions. We evaluate our approach on two distinct datasets: methane molecules and methanol adsorbed on a Cu(100) surface. The results demonstrate that EL-MLFFs significantly improves force prediction accuracy compared to individual MLFFs, with the ensemble of all eight models yielding the best performance. Moreover, our ablation study highlights the crucial roles of the residual network and graph attention layers in the model's architecture. The EL-MLFFs framework offers a promising solution to the challenges of model selection and force prediction accuracy in MLFFs, paving the way for more reliable and efficient molecular simulations.

Related papers

• Generalizability of Graph Neural Network Force Fields for Predicting Solid-State Properties [8.405078403907241]
  Machine-learned force fields (MLFFs) promise to offer a computationally efficient alternative to ab initio simulations for complex molecular systems. This work investigates the ability of a graph neural network (GNN)-based MLFF to describe solid-state phenomena not explicitly included during training.
  arXiv  Detail & Related papers  (2024-09-16T02:14:26Z)
• Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning [0.0]
  We introduce a Multi-Modal Fusion (MMF) framework that harnesses the analytical prowess of Graph Neural Networks (GNNs) and the linguistic generative and predictive abilities of Large Language Models (LLMs) Our framework combines the effectiveness of GNNs in modeling graph-structured data with the zero-shot and few-shot learning capabilities of LLMs, enabling improved predictions while reducing the risk of overfitting.
  arXiv  Detail & Related papers  (2024-08-27T11:10:39Z)
• On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness [16.595935469099306]
  We propose FLAME, an optimization framework by utilizing the alternating direction method of multipliers (ADMM) to train personalized and global models. Our theoretical analysis establishes the global convergence and two kinds of convergence rates for FLAME under mild assumptions. Our experimental findings show that FLAME outperforms state-of-the-art methods in convergence and accuracy, and it achieves higher test accuracy under various attacks.
  arXiv  Detail & Related papers  (2024-07-23T11:35:42Z)
• Enhancing Fast Feed Forward Networks with Load Balancing and a Master Leaf Node [49.08777822540483]
  Fast feedforward networks (FFFs) exploit the observation that different regions of the input space activate distinct subsets of neurons in wide networks. We propose the incorporation of load balancing and Master Leaf techniques into the FFF architecture to improve performance and simplify the training process.
  arXiv  Detail & Related papers  (2024-05-27T05:06:24Z)
• Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning [3.321322648845526]
  Machine learning interatomic potentials (MLIPs) have introduced a new paradigm for atomic simulations. Recent advancements have seen the emergence of universal MLIPs (uMLIPs) that are pre-trained on diverse materials datasets. We show that their performance in extrapolating to out-of-distribution complex atomic environments remains unclear.
  arXiv  Detail & Related papers  (2024-05-11T22:30:47Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• Improving and generalizing flow-based generative models with minibatch optimal transport [90.01613198337833]
  We introduce the generalized conditional flow matching (CFM) technique for continuous normalizing flows (CNFs) CFM features a stable regression objective like that used to train the flow in diffusion models but enjoys the efficient inference of deterministic flow models. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference.
  arXiv  Detail & Related papers  (2023-02-01T14:47:17Z)
• A Universal Framework for Featurization of Atomistic Systems [0.0]
  Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales. We introduce the Gaussian multi-pole (GMP) featurization scheme that utilizes physically-relevant multi-pole expansions of the electron density around atoms. We demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements.
  arXiv  Detail & Related papers  (2021-02-04T03:11:00Z)
• Machine Learning Force Fields [54.48599172620472]
  Machine Learning (ML) has enabled numerous advances in computational chemistry. One of the most promising applications is the construction of ML-based force fields (FFs) This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them.
  arXiv  Detail & Related papers  (2020-10-14T13:14:14Z)
• Training Deep Energy-Based Models with f-Divergence Minimization [113.97274898282343]
  Deep energy-based models (EBMs) are very flexible in distribution parametrization but computationally challenging. We propose a general variational framework termed f-EBM to train EBMs using any desired f-divergence. Experimental results demonstrate the superiority of f-EBM over contrastive divergence, as well as the benefits of training EBMs using f-divergences other than KL.
  arXiv  Detail & Related papers  (2020-03-06T23:11:13Z)
• FedDANE: A Federated Newton-Type Method [49.9423212899788]
  Federated learning aims to jointly learn low statistical models over massively distributed datasets. We propose FedDANE, an optimization that we adapt from DANE, to handle federated learning.
  arXiv  Detail & Related papers  (2020-01-07T07:44:41Z)

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.