Enhancing machine-learning interatomic potentials for advanced materials modeling

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

The Field That Fell 100 Years Behind: What Engineers Can Learn From The Textile Industry

Walk into a modern semiconductor factory and you’ll see full end-to-end automated processes. Visit an automotive plant and ...
eLife

How Occam’s razor guides human decision-making

Occam’s razor is the principle that, all else being equal, simpler explanations should be preferred over more complex ones. This principle is thought to guide human decision-making, but the nature of ...

NTT Scientists Contribute Fifteen Research Papers to NeurIPS 2025

NTT Research, Inc. and NTT R&D, divisions of NTT (TYO:9432), and NTT DATA, Inc. announced that NTT scientists and researchers have contributed to fifteen presentations at this year’s Conference on ...
GitHub

Maximum Entropy Deep Inverse Reinforcement Learning

This repository contains the implementation of the paper "Maximum Entropy Deep Inverse Reinforcement Learning" by Wulfmeier et al. [1] in PyTorch. You will also find in the notebooks directory a ...
Frontiers

Improved multi-scale divergence entropy combined with extreme learning machine classifier for rotating machinery fault recognition

Introduction: As the core equipment in industrial production, rotating machinery bearings play a critical role. However, traditional feature extraction algorithms for vibration signals are susceptible ...
GitHub

An interpretable machine learning framework for designing high-strength high-entropy alloys using feature selection, generative models, and ensemble learning.

There was an error while loading. Please reload this page. Data_preprocessing_and_feature_selction.py contains Python code focused on preprocessing data and selecting ...
UroToday

Harnessing Artificial Intelligence to Predict Spontaneous Stone Passage: Development and Testing of a Machine Learning-Based Calculator.

Objective: We sought to use artificial intelligence (AI) to develop and test calculators to predict spontaneous stone passage (SSP) using radiographical and clinical data. Methods: Consecutive ...
Frontiers

Cognitive constraints in bilingual processing—an entropy-based discrimination between translation and second language production

This study investigates to what extent second language production (L2) and translational production are influenced by cognitive constraints due to their bilingual processing. Compared with monolingual ...
EurekAlert!

First machine learning model developed to calculate the volume of all glaciers on Earth

VENICE – A team of researchers led by Niccolò Maffezzoli, “Marie Curie” fellow at Ca’ Foscari University of Venice and the University of California, Irvine, and an associate member of the Institute of ...