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Abstract Graph neural networks (GNNs) have excelled in predictive modeling for both crystals and molecules, owing to the expressiveness of graph representations. High-entropy alloys (HEAs), however, lack chemical long-range order, limiting the applicability of current graph representations. To overcome this challenge, we propose a representation of HEAs as a collection of local environment graphs. Based on this representation, we introduce the LESets machine learning model, an accurate, interpretable GNN for HEA property prediction. We demonstrate the accuracy of LESets in modeling the mechanical properties of quaternary HEAs. Through analyses and interpretation, we further extract insights into the modeling and design of HEAs. In a broader sense, LESets extends the potential applicability of GNNs to disordered materials with combinatorial complexity formed by diverse constituents and their flexible configurations. 

Abstract Artificial intelligence and machine learning frameworks have become powerful tools for establishing computationally efficient mappings between inputs and outputs in engineering problems. These mappings have enabled optimization and analysis routines, leading to innovative designs, advanced material systems, and optimized manufacturing processes. In such modeling efforts, it is common to encounter multiple information (data) sources, each varying in specifications. Data fusion frameworks offer the capability to integrate these diverse sources into unified models, enhancing predictive accuracy and enabling knowledge transfer. However, challenges arise when these sources are heterogeneous, i.e., they do not share the same input parameter space. Such scenarios occur when domains differentiated by complexity such as fidelity, operating conditions, experimental setup, and scale, require distinct parametrizations. To address this challenge, a two-stage heterogeneous multi-source data fusion framework based on the input mapping calibration (IMC) and the latent variable Gaussian process (LVGP) is proposed. In the first stage, the IMC algorithm transforms the heterogeneous input parameter spaces into a unified reference parameter space. In the second stage, an LVGP-enabled multi-source data fusion model constructs a single-source-aware surrogate model on the unified reference space. The framework is demonstrated and analyzed through three engineering modeling case studies with distinct challenges: cantilever beams with varying design parametrizations, ellipsoidal voids with varying complexities and fidelities, and Ti6Al4V alloys with varying manufacturing modalities. The results demonstrate that the proposed framework achieves higher predictive accuracy compared to both independent single-source and source-unaware data fusion models. 

Abstract Materials in crystalline form possess translational symmetry (TS) when the unit cell is repeated in real space with long‐ and short‐range orders. The periodic potential in the crystal regulates the electron wave function and results in unique band structures, which further define the physical properties of the materials. Amorphous materials lack TS due to the randomization of distances and arrangements between atoms, causing the electron wave function to lack a well‐defined momentum. High entropy materials provide another way to break the TS by randomizing the potential strength at periodic atomic sites. The local elemental distribution has a great impact on physical properties in high entropy materials. It is critical to distinguish elements at the sub‐nanometer scale to uncover the correlations between the elemental distribution and the material properties. Here, the use of synchrotron X‐ray scanning tunneling microscopy (SX‐STM) with sub‐nm scale resolution in identifying elements on a high entropy alloy (HEA) surface is demonstrated. By examining the elementally sensitive X‐ray absorption spectra with an STM tip to enhance the spatial resolution, the elemental distribution on an HEA's surface at a sub‐nm scale is extracted. These results open a pathway towards quantitatively understanding high entropy materials and their material properties. 

Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society2024 

A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected from crystals with random orientations. Inspired by the human decision-making process, a framework for automated crystal system classification from DPs with arbitrary orientations was developed. A convolutional neural network was trained using evidential deep learning, and the predictive uncertainties were quantified and leveraged to fuse multiview predictions. Using vector map representations of DPs, the framework achieves a testing accuracy of 0.94 in the examples considered, is robust to noise, and retains remarkable accuracy using experimental data. This work highlights the ability of ML to be used to accelerate experimental high-throughput materials data analytics.