Related papers: Revealing in-plane grain boundary composition feat…
Mg grain boundary (GB) segregation and GB diffusion can impact the processing and properties of Al-Mg alloys. Yet, Mg GB diffusion in Al has not been measured experimentally or predicted by simulations. We apply atomistic computer…
Graph Transformers have recently attracted attention for molecular property prediction by combining the inductive biases of graph neural networks (GNNs) with the global receptive field of Transformers. However, many existing hybrid…
Atomistic simulations have now established themselves as an indispensable tool in understanding deformation mechanisms of materials at the atomic scale. Large scale simulations are regularly used to study the behavior of polycrystalline…
Structural transformations at interfaces are of profound fundamental interest as complex examples of phase transitions in low-dimensional systems. Despite decades of extensive research, no compelling evidence exists for structural…
Grain boundaries (GBs) are ubiquitous in large-scale graphene samples, playing a crucial role in their overall performance. Due to their complexity, they are usually investigated as model structures, under the assumption of a fully relaxed…
Deep learning is an important method for molecular design and exhibits considerable ability to predict molecular properties, including physicochemical, bioactive, and ADME/T (absorption, distribution, metabolism, excretion, and toxicity)…
Grain boundary (GB) segregation models are derived for multi-principal element and high-entropy alloys (MPEAs and HEAs). Differing from classical models where one component is taken as solvent and others are considered solutes, these models…
Crystal structure determination from powder diffraction patterns is a complex challenge in materials science, often requiring extensive expertise and computational resources. This study introduces DiffractGPT, a generative pre-trained…
Grain boundary segregation controls properties of polycrystalline materials such as their susceptibility to intergranular cracking. It is of interest to engineer alloy chemistry to enhance grain boundary cohesion to prevent intergranular…
With the recent advances in machine learning for quantum chemistry, it is now possible to predict the chemical properties of compounds and to generate novel molecules. Existing generative models mostly use a string- or graph-based…
As datasets used in scientific applications become more complex, studying the geometry and topology of data has become an increasingly prevalent part of the data analysis process. This can be seen for example with the growing interest in…
Materials characterization and property measurements are a cornerstone of material science, providing feedback from synthesis to applications. Traditionally, a single sample is used to derive information on a single point in composition…
Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property can lead to significant advancements in scientific…
The space of possible grain boundary structures is vast, with 5 macroscopic, crystallographic degrees of freedom that define the character of a grain boundary. While numerous datasets of grain boundaries have examined this space in part or…
Segregation of alloying elements and impurities at grain boundaries (GBs) critically influences material behavior by affecting cohesion. In this study, we present an ab initio high-throughput evaluation of segregation energies and cohesive…
Advancing artificial intelligence for physical sciences requires representations that are both interpretable and compatible with the underlying laws of nature. We introduce METASTRINGS, a symbolic language for photonics that expresses…
Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text, and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum…
Grain boundaries (GB) profoundly influence the electrical properties of polycrystalline ionic solids. Yet, precise control of their transport characteristics has remained elusive, thereby limiting the performance of solid-state…
The electrical and optical properties of semiconductor materials are profoundly influenced by the atomic configurations and concentrations of intrinsic defects. This influence is particularly significant in the case of $\beta$-$\rm…
A Restricted Boltzmann Machine (RBM) is an unsupervised machine-learning bipartite graphical model that jointly learns a probability distribution over data and extracts their relevant statistical features. As such, RBM were recently…