A low-temperature prismatic slip instability in Mg understood using machine learning potentials

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Chatter is a harmful self-excited vibration that commonly occurs during milling processes. Data -driven chatter detection and prediction is critical to achieve high surface quality and process efficiency. Most existing chatter detection approaches are base ...

Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

Small scale ultrasonic piezoelectric actuators performance strongly depends on not well-known contact dynamics. Deep Neural Network (DNN) sees their use in physic simulation growing as their flexibility allows better performance especially when dynamics la ...

A central question of machine learning is how deep nets manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representation of the data where information irrelevant to the task is lost. For image da ...

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterog ...

Phase retrieval consists in the recovery of a complex-valued signal from intensity-only measurements. As it pervades a broad variety of applications, many researchers have striven to develop phase-retrieval algorithms. Classical approaches involve techniqu ...

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

Noise in the data is one of the main cause of model performance drop. Denoising is therefore a critical step in most data pipelines. In this paper we propose to fuse the learning abilities of Neural Network with the well-proven efficiency of the wavelet pa ...

Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural re ...

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

Metal-based Laser Powder Bed Fusion (LPBF) has made fabricating intricate components easier. Yet, assessing part quality is inefficient, relying on costly Computed Tomography (CT) scans or time-consuming destructive tests. Also, intermittent inspection of ...

A low-temperature prismatic slip instability in Mg understood using machine learning potentials

Chat with Graph Search

Exploring the effectiveness of using internal CNC system signals for chatter detection in milling process

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Normal Contact Force Estimation Using Deep Learning

How deep convolutional neural networks lose spatial information with training

Surface segregation in high-entropy alloys from alchemical machine learning

Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Robust time series denoising with learnable wavelet packet transform

Learnable latent embeddings for joint behavioural and neural analysis

Prediction rigidities for data-driven chemistry

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning