傳統的材料計算盡管能夠提供關于假設材料物理性質的準確信息，但仍具有一定的局限性。與傳統方法不同，材料信息學（MI）方法首先將原始數據描述轉換為可用于數學推理和推斷的適當表示。

Fig. 2 Overview of proposed SCANN architecture.

近年來，許多基于深度學習（DL）的MI方法被開發出來，以應對材料表示方面的挑戰并預測其物理特性。然而，目前在材料研究中使用的DL模型在提供用來解釋預測和理解材料構效關系的有效信息方面表現不足。

Fig. 3 Visualizations of structure–property relationships for molecules in QM9 dataset.

來自日本科學與技術高級研究所的Tien-Sinh Vu等，提出了一種可詮釋DL架構，該架構結合了注意力機制來預測材料特性并深入理解其構效關系。

Fig. 4 Correspondence between obtained GA scores of carbon, nitrogen, and oxygen atomic sites and molecular orbitals of molecular?structures in QM9 dataset.

作者使用兩個著名的數據集（QM9和Materials Project數據集），以及三個內部開發的計算材料數據集，對所提出的架構進行了評估。訓練–測試–分割驗證證實了使用DL架構導出的模型具有強大的預測能力，可與當前最先進的模型相媲美。

Fig. 5 Visualizations of structure–property relationships for fullerene molecules.

此外，基于第一性原理計算的比較驗證表明，在解釋與物理性質有關的構效關系時，原子的局部結構對材料結構表示的注意程度至關重要。這些性質包括分子軌道能量和晶體形成能。

Fig. 6 Visualization of relationship between the adsorption energy and the deformation of a graphene flake with a platinum atom adsorbed on a graphene flake.

通過預測材料性質并明確識別相應結構中的關鍵特征，本工作所提出的架構在加速材料設計方面顯示出了巨大的潛力。該文近期發布于npj Computational Materials 9: 215 (2023).

Editorial Summary

A central challenge in the field of materials science involves the use of both experience and theory to explore the compositions and structures of materials with specific properties and subsequently validating them through experimentation. Traditionally, materials have been characterized based on their elemental compositions and structures. Researchers have primarily relied on their knowledge and experience to predict certain properties of hypothetical materials with specific compositions and structures. Traditional material calculations can provide accurate information on the physical properties of hypothetical materials, but they still have certain limitations. Unlike traditional approaches, materials informatics (MI) approaches initially involve the conversion of primitive data descriptions into appropriate representations that can be used for mathematical reasoning and inference. Recently, various deep learning (DL)-based MI approaches have been developed to address the challenges associated with material representation and to predict physical properties.

However, DL models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material properties.?

Tien-Sinh Vu et al. from Japan Advanced Institute of Science and Technology, proposed an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gained insights into their structure–property relationships. The proposed architecture was evaluated using two well-known datasets (the QM9 and the Materials Project datasets), and three in-house-developed computational materials datasets. Train–test–split validations confirmed that the models derived using the proposed DL architecture exhibit strong predictive capabilities, which are comparable to those of current state-of-the-art models. Furthermore, comparative validations, based on first-principles calculations, indicated that the degree of attention of the atoms’ local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical properties. These properties encompass molecular orbital energies and the formation energies of crystals. The proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.?This article was recently published in npj Computational Materials 9: 215 (2023).

原文Abstract及其翻譯

Abstract

Deep learning (DL) models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material properties. To address these limitations, we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property relationships.

The proposed architecture is evaluated using two well-known datasets (the QM9 and the Materials Project datasets), and three in-house-developed computational materials datasets. Train–test–split validations confirm that the models derived using the proposed DL architecture exhibit strong predictive capabilities, which are comparable to those of current state-of-the-art models. Furthermore, comparative validations, based on first-principles calculations, indicate that the degree of attention of the atoms’ local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical properties. These properties encompass molecular orbital energies and the formation energies of crystals. The proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures.

摘要

目前，在材料研究中使用的深度學習（DL）模型在提供用來解釋預測和理解材料構效關系的有效信息方面表現出一定的局限性。為了解決這些局限，我們提出了一種可詮釋的DL架構，該架構結合了注意力機制來預測材料特性并深入理解其構效關系。我們使用兩個著名的數據集（QM9和Materials Project數據集），以及三個內部開發的計算材料數據集，對所提出的架構進行了評估。訓練–測試–分割驗證證實了使用DL架構導出的模型具有強大的預測能力，可與當前最先進的模型相媲美。此外，基于第一性原理計算的比較驗證表明，在解釋與物理性質有關的構效關系時，原子的局部結構對材料結構演示的注意程度至關重要。這些性質包括分子軌道能量和晶體形成能。通過預測材料性質并明確識別相應結構中的關鍵特征，本工作所提出的架構在加速材料設計方面顯示出了巨大的潛力。