Publication

Modeling the Ga/As binary system across temperatures and compositions from first principles

Related publications (30)

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

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 ...

EPFL2024

Field-controlled multicritical behavior and emergent universality in fully frustrated quantum magnets

Bruce Normand

Phase transitions in condensed matter are a source of exotic emergent properties. We study the fully frustrated bilayer Heisenberg antiferromagnet to demonstrate that an applied magnetic field creates a previously unknown emergent criticality. The quantum ...

Nature Portfolio2024

Hydrogen-induced tunable remanent polarization in a perovskite nickelate

Michele Kotiuga

Materials with field-tunable polarization are of broad interest to condensed matter sciences and solid-state device technologies. Here, using hydrogen (H) donor doping, we modify the room temperature metallic phase of a perovskite nickelate NdNiO3 into an ...

Nature Portfolio2024

Efficient and insightful descriptors for representing molecular and material space

Alexander Jan Goscinski

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

EPFL2024

Thermal transport of glasses via machine learning driven simulations

Federico Grasselli

Accessing the thermal transport properties of glasses is a major issue for the design of production strategies of glass industry, as well as for the plethora of applications and devices where glasses are employed. From the computational standpoint, the che ...

Frontiers Media Sa2024

Computational fluid dynamics simulations of the heat transfer properties of graphene-based nanolubricants and application to hydrodynamic lubrication

Roberto Guarino

In this paper, we consider experimental data available for graphene-based nanolubricants to evaluate their convective heat transfer performance by means of computational fluid dynamics (CFD) simulations. Single-phase models with temperature-dependent prope ...

Aip Publishing2024

Frustrated magnets in the limit of infinite dimensions: Dynamics and disorder-free glass transition

Achille Mauri

We study the statistical mechanics and the equilibrium dynamics of a system of classical Heisenberg spins with frustrated interactions on a d -dimensional simple hypercubic lattice, in the limit of infinite dimensionality d -> infinity . In the analysis we ...

Amer Physical Soc2024

Organic matters, but inorganic matters too: column examination of elevated mercury sorption on low organic matter aquifer material using concentrations and stable isotope ratios

Lorenz Schwab

Sorption of mercury (Hg) in soils is suggested to be predominantly associated with organic matter (OM). However, there is a growing collection of research that suggests that clay minerals and oxides are also important solid phases for the sorption of solu ...

2024

Localized Ionic Reinforcement of Double Network Granular Hydrogels

Esther Amstad, Alexandra Thoma

Nature produces soft materials with fascinating combinations of mechanical properties. For example, the mussel byssus embodies a combination of stiffness and toughness, a feature that is unmatched by synthetic hydrogels. Key to enabling these excellent mec ...

Wiley-V C H Verlag Gmbh2024

Magnetism of Single Surface Adsorbed Atoms Studied with Radio-Frequency STM

Clément Marie Soulard

This thesis investigates the magnetic properties of single atoms and molecules adsorbed on thin magnesium oxide decoupling layers, grown on a silver single crystal. To address these systems experimentally, we use a low temperature scanning tunneling micros ...

EPFL2024

Computational Studies on the Stability of Organic-Inorganic Hybrid Halide Lead Perovskites

Mathias Dankl

Perovskite-based solar cells are currently the most rapidly advancing photovoltaic technology but concerns about their long-term stability are still impeding full-scale commercialization. This thesis provides computational insights into some of the stabili ...

EPFL2024

High-order geometric integrators for the variational Gaussian wavepacket dynamics and application to vibronic spectra at finite temperature

Roya Moghaddasi Fereidani

Molecular quantum dynamics simulations are essential for understanding many fundamental phenomena in physics and chemistry. They often require solving the time-dependent Schrödinger equation for molecular nuclei, which is challenging even for medium-sized ...

EPFL2024

Optimizing Dynamic Aperture Studies with Active Learning

Tatiana Pieloni, Massimo Giovannozzi, Ekaterina Krymova, Mike Seidel, Davide Di Croce

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

Iop Publishing Ltd2024

Ion Spectroscopy Reveals Structural Difference for Proteins Microhydrated by Retention and Condensation of Water

Oleg Boyarkine, Andrei Zviagin

Protein ubiquitin in its +7 charge state microhydrated by 5 and 10 water molecules has been interrogated in the gas phase by cold ion UV/IR spectroscopy. The complexes were formed either by condensing water onto the unfolded bare proteins in a temperature- ...

Amer Chemical Soc2024

Random matrix methods for high-dimensional machine learning models

Antoine Philippe Michel Bodin

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

EPFL2024

Mixed ionic-electronic conduction in Ruddlesden-Popper and Dion-Jacobson layered hybrid perovskites with aromatic organic spacers

Michael Graetzel, Davide Moia, Joachim Maier, Jovana Milic, Algirdas Ducinskas, Mina Jung

The understanding of mixed ionic-electronic conductivity in hybrid perovskites has enabled major advances in the development of optoelectronic devices based on this class of materials. While recent investigations revealed the potential of using dimensional ...

Royal Society of Chemistry2024

Phase diagram of the J-Jd Heisenberg model on the maple leaf lattice: Neural networks and density matrix renormalization group

Ronny Thomale, Pratyay Ghosh

We microscopically analyze the nearest-neighbor Heisenberg model on the maple leaf lattice through neural quantum state (NQS) and infinite density matrix renormalization group (iDMRG) methods. Embarking to parameter regimes beyond the exact dimer singlet g ...

Amer Physical Soc2024

Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy

Michele Ceriotti, Federico Grasselli

The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)he ...

Amer Physical Soc2024

Molecular intercalation in the van der Waals antiferromagnets FePS3 and NiPS3

Bruce Normand, Xiaoyu Xu, Sheng Xu, Ying Chen, Zeyu Wang, Shuo Li, Weiqiang Yu

We have performed electrochemical treatment of the van der Waals antiferromagnetic materials FePS3 and NiPS3 with the ionic liquid EMIM-BF4, achieving significant molecular intercalation. Mass analysis of the intercalated compounds, EMIMx-FePS3 and EMIMx-N ...

Amer Physical Soc2024

Towards Scalable Electronics: Synthetic 2D Materials for Large-Area 2D Circuit Integration

Zhenyu Wang

In the past decades, a significant increase of the transistor density on a chip has led to exponential growth in computational power driven by Moore's law. To overcome the bottleneck of traditional von-Neumann architecture in computational efficiency, effo ...

EPFL2024