Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study

• URL: http://arxiv.org/abs/2401.08032v1
• Date: Tue, 16 Jan 2024 01:03:39 GMT
• Title: Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
• Authors: Sadman Sadeed Omee and Nihang Fu and Rongzhi Dong and Ming Hu and Jianjun Hu
• Abstract summary: We present a benchmark study of structure-based graph neural networks (GNNs) for extrapolative OOD materials property prediction. Our experiments show that current state-of-the-art GNN algorithms significantly underperform for the OOD property prediction tasks. We identify the sources of CGCNN, ALIGNN, and DeeperGATGNN's significantly more robust OOD performance than those of the current best models.
• Score: 1.3711992220025948
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In real-world material research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the known materials. It is thus a pressing question to provide an objective evaluation of ML model performances in property prediction of out-of-distribution (OOD) materials that are different from the training set distribution. Traditional performance evaluation of materials property prediction models through random splitting of the dataset frequently results in artificially high performance assessments due to the inherent redundancy of typical material datasets. Here we present a comprehensive benchmark study of structure-based graph neural networks (GNNs) for extrapolative OOD materials property prediction. We formulate five different categories of OOD ML problems for three benchmark datasets from the MatBench study. Our extensive experiments show that current state-of-the-art GNN algorithms significantly underperform for the OOD property prediction tasks on average compared to their baselines in the MatBench study, demonstrating a crucial generalization gap in realistic material prediction tasks. We further examine the latent physical spaces of these GNN models and identify the sources of CGCNN, ALIGNN, and DeeperGATGNN's significantly more robust OOD performance than those of the current best models in the MatBench study (coGN and coNGN), and provide insights to improve their performance.

Related papers

• Out-of-distribution materials property prediction using adversarial learning based fine-tuning [0.0]
  We propose an adversarial learning based targeting finetuning approach to make the model adapted to a particular dataset. Our experiments demonstrate the success of our CAL algorithm with its high effectiveness in ML with limited samples.
  arXiv  Detail & Related papers  (2024-08-17T21:22:21Z)
• Mining experimental data from Materials Science literature with Large Language Models: an evaluation study [1.9849264945671101]
  This study is dedicated to assessing the capabilities of large language models (LLMs) in extracting structured information from scientific documents in materials science. We focus on two critical tasks of information extraction: (i) a named entity recognition (NER) of studied materials and physical properties and (ii) a relation extraction (RE) between these entities. The performance of LLMs in executing these tasks is benchmarked against traditional models based on the BERT architecture and rule-based approaches (baseline)
  arXiv  Detail & Related papers  (2024-01-19T23:00:31Z)
• Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction [6.349503549199403]
  We introduce our model Materials Informatics Transformer (MatInFormer) for material property prediction. Specifically, we introduce a novel approach that involves learning the grammar of crystallography through the tokenization of pertinent space group information.
  arXiv  Detail & Related papers  (2023-08-30T18:34:55Z)
• Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations [111.88727295707454]
  This paper reexamines the research on out-of-distribution (OOD) robustness in the field of NLP. We propose a benchmark construction protocol that ensures clear differentiation and challenging distribution shifts. We conduct experiments on pre-trained language models for analysis and evaluation of OOD robustness.
  arXiv  Detail & Related papers  (2023-06-07T17:47:03Z)
• Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding Models [68.12229916000584]
  We develop an out-of-distribution (OOD) benchmark termed Do-GOOD for the fine-Grained analysis on Document image-related tasks. We then evaluate the robustness and perform a fine-grained analysis of 5 latest VDU pre-trained models and 2 typical OOD generalization algorithms.
  arXiv  Detail & Related papers  (2023-06-05T06:50:42Z)
• Pseudo-OOD training for robust language models [78.15712542481859]
  OOD detection is a key component of a reliable machine-learning model for any industry-scale application. We propose POORE - POsthoc pseudo-Ood REgularization, that generates pseudo-OOD samples using in-distribution (IND) data. We extensively evaluate our framework on three real-world dialogue systems, achieving new state-of-the-art in OOD detection.
  arXiv  Detail & Related papers  (2022-10-17T14:32:02Z)
• SimSCOOD: Systematic Analysis of Out-of-Distribution Generalization in Fine-tuned Source Code Models [58.78043959556283]
  We study the behaviors of models under different fine-tuning methodologies, including full fine-tuning and Low-Rank Adaptation (LoRA) fine-tuning methods. Our analysis uncovers that LoRA fine-tuning consistently exhibits significantly better OOD generalization performance than full fine-tuning across various scenarios.
  arXiv  Detail & Related papers  (2022-10-10T16:07:24Z)
• Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection [55.028065567756066]
  Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-world applications. In this paper we propose an uncertainty quantification approach by modelling the distribution of features. We incorporate an efficient ensemble mechanism, namely batch-ensemble, to construct the batch-ensemble neural networks (BE-SNNs) and overcome the feature collapse problem. We show that BE-SNNs yield superior performance on several OOD benchmarks, such as the Two-Moons dataset, the FashionMNIST vs MNIST dataset, FashionM
  arXiv  Detail & Related papers  (2022-06-26T16:00:22Z)
• A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements [3.65211252467094]
  We present a data-driven correlation to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios. A material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve leaflet. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material properties learned implicitly from the data and naturally embedded in the network parameters.
  arXiv  Detail & Related papers  (2022-04-01T04:56:41Z)
• Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study [81.11161697133095]
  We take the NER task as a testbed to analyze the generalization behavior of existing models from different perspectives. Experiments with in-depth analyses diagnose the bottleneck of existing neural NER models. As a by-product of this paper, we have open-sourced a project that involves a comprehensive summary of recent NER papers.
  arXiv  Detail & Related papers  (2020-01-12T04:33:53Z)

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.