The rule of four: anomalous distributions in the stoichiometries of inorganic compounds

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.

Pyrene-based metal-organic frameworks (MOFs) have tremendous potential for various applications. With infinite structural possibilities, the MOF community often relies on simulations to identify the most promising candidates for given applications. Among t ...

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

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 ...

Given a ground-level query image and a geo-referenced aerial image that covers the query’s local surroundings, fine-grained cross-view localization aims to estimate the location of the ground camera inside the aerial image. Recent works have focused on dev ...

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fr ...

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 ...

Structure determinationof molecular solids through NMR crystallographyrelies on the generation of a comprehensive set of candidate crystalstructures and on the comparison of chemical shifts computed for thosecandidates with experimental values. Exploring t ...

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 ...

Barocaloric (BC) materials provide cheaper and more energy efficient alternatives to traditional refrigerants. Some liquid alkanes were recently shown to exhibit a colossal BC effect, matching the entropy changes in commercial vapour-liquid refrigerants. D ...

Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

Many properties of materials, including their dissolution kinetics, hardness, and optical appearance, depend on their structure. Unfortunately, it is often difficult to control the structure of low molecular weight organic compounds that have a high propen ...

In the third study, the order-disorder phase transition of single-crystal C60 is studied using time-resolved electron microscopy. Solid C60 undergoes a first-order phase transition upon heating to 260 K. This is a structural transition from the low-tempera ...

Structure determination of materials is key to understanding their physical properties. While single-crystal X-ray diffraction is the gold standard for structures displaying long-range order, many materials of interest are polycrystalline and/or disordered ...

Diffusion is one of the most important phenomena studied in science ranging from physics to biology and, in abstract form, even in social sciences. In the field of materials science, diffusion in crystalline solids is of particular interest as it plays a p ...

We mine from the literature experimental data on the CO2 electrochemical reduction selectivity of Cu single crystal surfaces. We then probe the accuracy of a machine learning model trained to predict faradaic efficiencies for 11 CO2 reduction reaction prod ...

Low-temperature variable-energy electron irradiation was used to induce non-magnetic disorder in a single crystal of a hole-doped iron-based superconductor, Ba1-xKxFe2As2, x = 0.80. To avoid systematic errors, the beam energy was adjusted non-consequently ...

Discovering new materials is essential but challenging, time-consuming, and expensive. In many cases, simulations can be useful for estimating material properties. For many of the most interesting properties, however, simulations are infeasible because of ...

The subject of the present work is discovery and in-depth characterization of a new class of functional materials. Tuning of the bond polarity and orbital occupation with a goal of establishing balance between localization and delocalization of electrons - ...

The rule of four: anomalous distributions in the stoichiometries of inorganic compounds

Chat with Graph Search

Unraveling metal effects on CO2 uptake in pyrene-based metal-organic frameworks

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

Adapting Fine-Grained Cross-View Localization to Areas Without Fine Ground Truth

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

A data-driven interpretation of the stability of organic molecular crystals

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

Chemical Shift-Dependent Interaction Maps in Molecular Solids

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

Pressure-freezing of dodecane: exploring the crystal structures, formation kinetics and phase diagrams for colossal barocaloric effects in n-alkanes

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Controlling the crystal structure of succinic acid via microfluidic spray-drying

Direct Observation of the Phase Transition in C60 Fullerene

NMR Crystallography in the Big Data Era: New Methods and Applications Powered by Machine Learning

Atomic scale volume and grain boundary diffusion elucidated by in situ STEM

What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO2 reduction? A data-driven retrospective

Ion-Selective Scattering Studied Using the Variable-Energy Electron Irradiation in the Ba0.2K0.8Fe2As2 Superconductor

Toward data-driven materials design: From atoms to pilot plants

Murunskite - a new class of functional material