Related papers: AFLOW: An automatic framework for high-throughput …

Machine learning techniques are increasingly used to predict material behavior in scientific applications and offer a significant advantage over conventional numerical methods. In this work, an Artificial Neural Network (ANN) model is used…

Recent network traffic classification methods benefitfrom machine learning (ML) technology. However, there aremany challenges due to use of ML, such as: lack of high-qualityannotated datasets, data-drifts and other effects causing aging…

The emergence of data-driven computational materials science offers unprecedented opportunities to explore complex material landscapes, complementing experimental research with the discovery of novel compounds. To enable these developments,…

It is difficult to quantify structure-property relationships and to identify structural features of complex materials. The characterization of amorphous materials is especially challenging because their lack of long-range order makes it…

With advancements in computational molecular modeling and powerful structure search methods, it is now possible to systematically screen crystal structures for small organic molecules. In this context, we introduce the Python package…

Metal-insulator transition (MIT) materials are a useful platform for emerging microelectronic, optoelectronic, and neuromorphic devices, but their discovery is hindered by the high computational cost of electronic structure modeling, the…

Large Language Model (LLM)-based multi-agent systems (MAS) demonstrate remarkable potential for scientific discovery. Existing approaches, however, often automate scientific discovery using predefined workflows that lack rationality…

We present exa-AMD, an open-source, high-performance framework designed for accelerated materials discovery on modern supercomputers. exa-AMD overcomes key computational bottlenecks in large-scale structure prediction through task-based…

Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pipelines. However, users tend to mistrust the optimization…

Flow matching models have emerged as a powerful framework for realistic image generation by learning to reverse a corruption process that progressively adds Gaussian noise. However, because noise is injected in the latent domain, its impact…

The discovery and design of new materials are paramount in the development of green technologies. High entropy oxides represent one such group that has only been tentatively explored, mainly due to the inherent problem of navigating vast…

Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two million materials…

Data scarcity remains a central challenge in materials discovery, where finding meaningful descriptors and tuning models for generalization is critical but inherently a discrete optimization problem prone to multiple local minima…

Progress in functional materials discovery has been accelerated by advances in high throughput materials synthesis and by the development of high-throughput computation. However, a complementary robust and high throughput structural…

Most of the knowledge in materials science literature is in the form of unstructured data such as text and images. Here, we present a framework employing natural language processing, which automates text and image comprehension and…

Radio astronomy observatories with high throughput back end instruments require real-time data processing. While computing hardware continues to advance rapidly, development of real-time processing pipelines remains difficult and…

Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a key stumbling block. Current methods typically use…

NetFlow data is a popular network log format used by many network analysts and researchers. The advantages of using NetFlow over deep packet inspection are that it is easier to collect and process, and it is less privacy intrusive. Many…

Proof autoformalization, the task of translating natural language theorems and proofs into machine-verifiable code, is a critical step for integrating large language models into rigorous mathematical workflows. Current approaches focus on…

Given the increasing adoption of AI solutions in professional environments, it is necessary for developers to be able to make informed decisions about the current tool landscape. This work empirically evaluates various MLOps (Machine…