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Toward a Materials Genome Approach for Ionic Liquids: Synthesis Guided by Ab Initio Property Maps

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Publications associées (38)

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Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins

Anne-Florence Raphaëlle Bitbol

Author summaryWhen two protein families interact, their sequences feature statistical dependencies. First, interacting proteins tend to share a common evolutionary history. Second, maintaining structure and interactions through the course of evolution yiel ...

PUBLIC LIBRARY SCIENCE2023

Systems and methods for de novo design of protein interactions with learned surface fingerprints

Pablo Gainza Cirauqui, Michael Bronstein, Zander Harteveld, Andreas Scheck, Sarah Wehrle, Anthony Marchand, Alexandra Krina Van Hall-Beauvais, Freyr Sverrisson

The present application relates to a computer-implemented systems and methods for protein interaction design using surface fingerprints. The method comprises predicting at least one target interface site with high binding propensity, wherein, optionally, t ...

2023

Influence of sun cups on surface albedo of wet Alpine snowpack

Loïc Marcel Louis Brouet, Michael Lehning

In high elevation Alpine areas, characterised by high snow accumulation and radiation-driven melt processes, the formation of peculiar ablation features called sun cups can be observed. Sun cups likely influence the energy and mass balance of the wet snowp ...

2024

Atomic-level structure determination of amorphous molecular solids by NMR

David Lyndon Emsley, Arthur César Pinon, Manuel Cordova, Pinelopi Moutzouri

Structure determination of amorphous materials remains challenging, owing to the disorder inherent to these materials. Nuclear magnetic resonance (NMR) powder crystallography is a powerful method to determine the structure of molecular solids, but disorder ...

Nature Portfolio2023

Why computational complexity may set impenetrable barriers for epistemic reductionism

Michael Herzog, Adrien Christophe Doerig, Christian Sachse

According to physicalism, everything is physical or metaphysically connected to the physical. If physicalism were true, it seems that we should - in principle - be able to reduce the descriptions and explanations of special sciences to physical ones, for e ...

Springer2023

Lessons learned from a prediction and postdiction of a shake table test on an unreinforced masonry aggregate

Katrin Beyer, Igor Tomic

Historical centers of Europe and Croatia are often formed by unreinforced masonry building aggregates that developed as the layout of the city or village was densified. In these aggregates, adjacent buildings can share structural walls with an older and a ...

2023

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

Kevin Rossi

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 ...

AMER PHYSICAL SOC2023

Modeling the mechanics and rheology of porous and granular media: an elastoplastic continuum approach

Lars Kristoffer Uhlen Blatny

The complex mechanics of porous and granular media play a significant role in various industrial processes and natural phenomena. As an example, understanding the mechanics of how failure occurs, localizes and propagates in porous brittle solids under vari ...

EPFL2023

Spectral and thermodynamic properties of interacting electrons with dynamical functionals

Tommaso Chiarotti

Empowered by ever-increasing computational power and algorithmic developments, electronic-structure simulations continue to drive research and innovation in materials science. In this context, ab-initio calculations offer an unbiased platform for the under ...

EPFL2023

Artificial intelligence for natural product drug discovery

Philippe Schwaller, Albert Hofstetter, Michael Alexander Skinnider, Florian Huber

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to e ...

Nature Portfolio2023

A Geometric Transformer for Structural Biology: Development and Applications of the Protein Structure Transformer

Lucien Fabrice Krapp

Proteins, the central building blocks of life, play pivotal roles in nearly every biological function. To do so, these macromolecular structures interact with their surrounding environment in complex ways, leading to diverse functional behaviors. The predi ...

EPFL2023

Scaling Description of Dynamical Heterogeneity and Avalanches of Relaxation in Glass-Forming Liquids

Matthieu Wyart

We provide a theoretical description of dynamical heterogeneities in glass-forming liquids, based on the premise that relaxation occurs via local rearrangements coupled by elasticity. In our framework, the growth of the dynamical correlation length e and o ...

American Physical Society2023

Machine-learned interatomic potentials: Recent developments and prospective applications

William Curtin

High-throughput generation of large and consistent ab initio data combined with advanced machine-learning techniques are enabling the creation of interatomic potentials of near ab initio quality. This capability has the potential of dramatically impacting ...

Springer Heidelberg2023

CO2 Capture and Release in Amine Solutions: To What Extent Can Molecular Simulations Help Understand the Trends?

Wanda Andreoni, Fabio Pietrucci, Changru Ma

Absorption in amine solutions is a well-established advanced technology for CO2 capture. However, the fundamental aspects of the chemical reactions occurring in solution still appear to be unclear. Our previous investigation of aqueous monoethanolamine (ME ...

2023

MiMiCPy: An Efficient Toolkit for MiMiC-Based QM/MM Simulations

Ursula Röthlisberger, Sophia Kathryn Johnson, Andrea Levy

MiMiC is a highly flexible, extremely scalable multiscale modeling framework. It couples the CPMD (quantum mechanics, QM) and GROMACS (molecular mechanics, MM) codes. The code requires preparing separate input files for the two programs with a selection of ...

AMER CHEMICAL SOC2023

Universal machine learning for the response of atomistic systems to external fields

Yaolong Zhang

Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in curren ...

Nature Portfolio2023

Improving Deepfake Detectors against Real-world Perturbations with Amplitude-Phase Switch Augmentation

Touradj Ebrahimi, Yuhang Lu

In recent years, the remarkable progress in facial manipulation techniques has raised social concerns due to their potential malicious usage and has received considerable attention from both industry and academia. While current deep learning-based face for ...

2023

Chemical Shift-Dependent Interaction Maps in Molecular Solids

David Lyndon Emsley, Manuel Cordova

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 ...

AMER CHEMICAL SOC2023