Machine learning interatomic potentials are revolutionizing large-scale,
accurate atomistic modelling in material science and chemistry. These
potentials often use atomic cluster expansion or equivariant message passing
with spherical harmonics as basis functions. However, the dependence on
Clebsch-Gordan coefficients for maintaining rotational symmetry leads to
computational inefficiencies and redundancies. We propose an alternative: a
Cartesian-coordinates-based atomic density expansion. This approach provides a
complete description of atomic environments while maintaining interaction body
orders. Additionally, we integrate low-dimensional embeddings of various
chemical elements and inter-atomic message passing. The resulting potential,
named Cartesian Atomic Cluster Expansion (CACE), exhibits good accuracy,
stability, and generalizability. We validate its performance in diverse
systems, including bulk water, small molecules, and 25-element high-entropy
alloys.

机器学习原子间势场，通过 Cartesian Atomic Cluster Expansion 模型在材料科学和化学的大规模、准确的原子模拟中起到了革命性的作用。该模型采用原子簇展开和等变信息传递与球谐函数作为基函数的方式，并结合了各种化学元素的低维嵌入和原子间信息传递。结果表明，该模型 (CACE) 具有良好的准确性、稳定性和普适性，并在不同系统中进行了验证，包括大量的水、小分子和 25 种元素的高熵合金。

基于笛卡尔原子团簇展开的机器学习金属间势能

Cartesian atomic cluster expansion for machine learning interatomic  potentials

Using machine learning (ML) techniques to predict material properties is a
crucial research topic. These properties depend on numerical data and semantic
factors. Due to the limitations of small-sample datasets, existing methods
typically adopt ML algorithms to regress numerical properties or transfer other
pre-trained knowledge graphs (KGs) to the material. However, these methods
cannot simultaneously handle semantic and numerical information. In this paper,
we propose a numerical reasoning method for material KGs (NR-KG), which
constructs a cross-modal KG using semantic nodes and numerical proxy nodes. It
captures both types of information by projecting KG into a canonical KG and
utilizes a graph neural network to predict material properties. In this
process, a novel projection prediction loss is proposed to extract semantic
features from numerical information. NR-KG facilitates end-to-end processing of
cross-modal data, mining relationships and cross-modal information in
small-sample datasets, and fully utilizes valuable experimental data to enhance
material prediction. We further propose two new High-Entropy Alloys (HEA)
property datasets with semantic descriptions. NR-KG outperforms
state-of-the-art (SOTA) methods, achieving relative improvements of 25.9% and
16.1% on two material datasets. Besides, NR-KG surpasses SOTA methods on two
public physical chemistry molecular datasets, showing improvements of 22.2% and
54.3%, highlighting its potential application and generalizability. We hope the
proposed datasets, algorithms, and pre-trained models can facilitate the
communities of KG and AI for materials.

利用机器学习技术预测材料性质是一个关键的研究课题。本文提出了一种数值推理方法用于材料知识图谱，通过构建跨模态知识图谱并利用图神经网络来预测材料性质，实现了处理跨模态数据、挖掘关系和跨模态信息、充分利用实验数据的目标。文章还引入了两个新的高熵合金属性数据集，并展示了该方法在材料数据集和物理化学分子数据集上相对于最先进方法的改进，突出了其潜在应用和普适性。希望提出的数据集、算法和预训练模型能促进知识图谱和人工智能在材料领域的发展。

跨模态知识图谱的综合数理推理方法：材料性质预测

Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of  Cross-modal Knowledge Graph for Material Property Prediction

The reason behind the remarkable properties of High-Entropy Alloys (HEAs) is
rooted in the diverse phases and the crystal structures they contain. In the
realm of material informatics, employing machine learning (ML) techniques to
classify phases and crystal structures of HEAs has gained considerable
significance. In this study, we assembled a new collection of 1345 HEAs with
varying compositions to predict phases. Within this collection, there were 705
sets of data that were utilized to predict the crystal structures with the help
of thermodynamics and electronic configuration. Our study introduces a
methodical framework i.e., the Pearson correlation coefficient that helps in
selecting the strongly co-related features to increase the prediction accuracy.
This study employed five distinct boosting algorithms to predict phases and
crystal structures, offering an enhanced guideline for improving the accuracy
of these predictions. Among all these algorithms, XGBoost gives the highest
accuracy of prediction (94.05%) for phases and LightGBM gives the highest
accuracy of prediction of crystal structure of the phases (90.07%). The
quantification of the influence exerted by parameters on the model's accuracy
was conducted and a new approach was made to elucidate the contribution of
individual parameters in the process of phase prediction and crystal structure
prediction.

利用机器学习技术预测高熵合金的相和晶体结构，采用相关系数选择高预测准确度的特征，并使用五种不同的提升算法提供了改进相和晶体结构预测精确度的指南。其中，XGBoost 对相的预测准确度达到 94.05％，LightGBM 对晶体结构的预测准确度达到 90.07％，并对模型准确度受参数影响的量化及单个参数在相和晶体结构预测过程中的贡献提出了新方法。

利用热力学和结构参数的强化机器学习框架提升高熵合金相位和晶体结构预测

A Boosted Machine Learning Framework for the Improvement of Phase and  Crystal Structure Prediction of High Entropy Alloys Using Thermodynamic and  Configurational Parameters