Materials Representation and Transfer Learning for Multi-Property Prediction

• URL: http://arxiv.org/abs/2106.02225v2
• Date: Mon, 7 Jun 2021 05:36:36 GMT
• Title: Materials Representation and Transfer Learning for Multi-Property Prediction
• Authors: Shufeng Kong, Dan Guevarra, Carla P. Gomes, John M. Gregoire
• Abstract summary: The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements. We introduce the Hierarchical Correlation Learning for Multi-property Prediction framework that seamlessly integrates (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning.
• Score: 22.068267502715404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.

Related papers

• Extrapolative ML Models for Copolymers [1.901715290314837]
  Machine learning models have been progressively used for predicting materials properties. These models are inherently interpolative, and their efficacy for searching candidates outside a material's known range of property is unresolved. Here, we determine the relationship between the extrapolation ability of an ML model, the size and range of its training dataset, and its learning approach.
  arXiv  Detail & Related papers  (2024-09-15T11:02:01Z)
• 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)
• Advancing Extrapolative Predictions of Material Properties through Learning to Learn [1.3274508420845539]
  We use attention-based architecture of neural networks and meta-learning algorithms to acquire extrapolative generalization capability. We highlight the potential of such extrapolatively trained models, particularly with their ability to rapidly adapt to unseen material domains.
  arXiv  Detail & Related papers  (2024-03-25T09:30:19Z)
• Tackling Computational Heterogeneity in FL: A Few Theoretical Insights [68.8204255655161]
  We introduce and analyse a novel aggregation framework that allows for formalizing and tackling computational heterogeneous data. Proposed aggregation algorithms are extensively analyzed from a theoretical, and an experimental prospective.
  arXiv  Detail & Related papers  (2023-07-12T16:28:21Z)
• Predicting Oxide Glass Properties with Low Complexity Neural Network and Physical and Chemical Descriptors [1.8734449181723827]
  We present a low complexity neural network (LCNN) that provides improved performance in predicting the properties of oxide glasses. By training on a large dataset (50000) of glass components, we show the LCNN outperforms state-of-the-art algorithms such as XGBoost. We demonstrate the universality of the LCNN models by predicting the properties for glasses with new components that were not present in the original training set.
  arXiv  Detail & Related papers  (2022-10-19T12:23:30Z)
• Pre-training via Denoising for Molecular Property Prediction [53.409242538744444]
  We describe a pre-training technique that utilizes large datasets of 3D molecular structures at equilibrium. Inspired by recent advances in noise regularization, our pre-training objective is based on denoising.
  arXiv  Detail & Related papers  (2022-05-31T22:28:34Z)
• Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport [49.237577649802034]
  We develop a novel optimal transport-based algorithm termed MROT to enhance their generalization capability for molecular regression problems. MROT significantly outperforms state-of-the-art models, showing promising potential in accelerating the discovery of new substances.
  arXiv  Detail & Related papers  (2022-02-13T04:56:18Z)
• Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery [0.7695660509846216]
  We introduce a transfer learning approach in which we seed models trained on a large amount of data from one row of the periodic table with a small number of data points from the additional row. We demonstrate the synergistic value of the eRACs alongside this transfer learning strategy to consistently improve model performance.
  arXiv  Detail & Related papers  (2021-06-20T22:34:04Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• Multilinear Compressive Learning with Prior Knowledge [106.12874293597754]
  Multilinear Compressive Learning (MCL) framework combines Multilinear Compressive Sensing and Machine Learning into an end-to-end system. Key idea behind MCL is the assumption of the existence of a tensor subspace which can capture the essential features from the signal for the downstream learning task. In this paper, we propose a novel solution to address both of the aforementioned requirements, i.e., How to find those tensor subspaces in which the signals of interest are highly separable?
  arXiv  Detail & Related papers  (2020-02-17T19:06:05Z)
• Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts [80.69440684790925]
  DeepRelations is a physics-inspired deep relational network with intrinsically explainable architecture. It shows superior interpretability to the state-of-the-art. It boosts the AUPRC of contact prediction 9.5, 16.9, 19.3 and 5.7-fold for the test, compound-unique, protein-unique, and both-unique sets.
  arXiv  Detail & Related papers  (2019-12-29T00:14:07Z)

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.