Completeness of Atomic Structure Representations

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.

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer ...

Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and dep ...

Machine learning is increasingly integrated with decision making by powering automated systems and informing human decision makers in applications such as CV screening, portfolio allocation, loan risk assessment, and insurance approval. In this thesis, I e ...

Object detection, a fundamental task in computer vision, is crucial for various intelligent edge computing applications. However, object detection algorithms are usually heavy in computation, hindering their deployments on resource-constrained edge devices ...

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 ...

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 ...

Computational chemistry techniques offer tremendous potential for accelerating the discovery of sustainable chemical processes and reactions. However, the environmental impacts of the substantial computing power required for these digital methods are often ...

The field of text-to-video retrieval has advanced significantly with the evolution of language models and large-scale pre-training on generated caption-video pairs. Current methods predominantly focus on visual and event-based details, making retrieval lar ...

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 ...

Raw data and Python code for the manuscript "Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution". All data files and source code are provided in a zip file. .ipynb file is a python jupyter notebook file whi ...

A large variety of new physics models suggest that the rates for lepton flavour violating

b

-hadron decays may be much higher than predicted in the Standard Model, which leads to a high interest in the search for such decays. This thesis presents the se ...

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preservi ...

Adversarial Training using a strong first-order adversary (PGD) is the gold standard for training Deep Neural Networks that are robust to adversarial examples. We show that, contrary to the general understanding of the method, the gradient at an optimal ad ...

The charge-parity (CP) structure of the Yukawa interaction between the Higgs (H) boson and the top quark is measured in a data sample enriched in the t (t) over bar H and tH associated production, using 138 fb(-1) of data collected in proton-proton collisi ...

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

Prismatic slip in magnesium at temperatures T

Background and Objective: Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with contagious diseases, many research teams have turned ...

Ideal magnetic frustration forms the basis for the emergence of exotic quantum spin states that are entirely nonmagnetic. Such singlet spin states are the defining feature of the Shastry-Sutherland model, and of its faithful materials realization in the qu ...

A search for the lepton-flavour violating decays B0 -> K*0µ(±)e(∓) and B(s)0-> ϕµ(±)e(∓) is presented, using proton-proton collision data collected by the LHCb detector at the LHC, corresponding to an integrated luminosity of 9 fb(-1). No significant signa ...

Completeness of Atomic Structure Representations

Chat with Graph Search

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

On the added value of sequential deep learning for the upscaling of evapotranspiration

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Machine Learning in Decision-Making Systems: Fairness, Robustness, and Data Bias

Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator

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

Surface segregation in high-entropy alloys from alchemical machine learning

Balancing computational chemistry's potential with its environmental impact

Beyond Essentials: Nuanced and Diverse Text-to-video Retrieval

Prediction rigidities for data-driven chemistry

Emergent coherent modes in nonlinear magnonic waveguides detected at ultrahigh frequency resolution

Search for the lepton flavour violating decays B0→e±μ∓ and B0s→e±μ∓ with LHCb Run 2 data

Live-cell imaging powered by computation

Finding Actual Descent Directions For Adversarial Training

Search for CP violation in t(t)over-bar H and tH production in multilepton channels in proton-proton collisions at √s=13 TeV

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

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

A semi-supervised algorithm for improving the consistency of crowdsourced datasets: The COVID-19 case study on respiratory disorder classification

Ultrafast frustration breaking and magnetophononic driving of singlet excitations in a quantum magnet

Search for the lepton-flavour violating decays B0 -> K*0µ(±)e(∓) and B(s)0-> ϕµ(±)e(∓)