Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

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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 ...

Interatomic potentials are essential for studying fundamental mechanisms of deformation and failure in metals and alloys because the relevant defects (dislocations, cracks, etc.) are far above the scales accessible to first-principles studies. Existing pot ...

We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H2O2 and CH3SO3H in phenol as an example. To address the challenges ...

High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic scale phenomena, particularly during ...

The molecular dipole polarizability describes the tendency of a molecule to change its dipole moment in response to an applied electric field. This quantity governs key intra- and intermolecular interactions, such as induction and dispersion; plays a vital ...

The present invention concerns a photocatalytic system comprising or consisting of: a titanium-based metal-organic framework MOF based on or including a single ligand type or a plurality of different ligand types for generating photo-generated charge carri ...

Using classical molecular dynamics simulations, we investigate the dielectric properties at interfaces of water with graphene, graphite, hexane, and water vapor. For graphite, we compare metallic and nonmetallic versions. At the vapor-liquid water and hexa ...

Automated analyses of the outcome of a simulation have been an important part of atomistic modeling since the early days, addressing the need of linking the behavior of individual atoms and the collective properties that are usually the final quantity of i ...

Objective To investigate the utility of an automatic deep learning (DL) method for segmentation of T2 maps in patients with idiopathic inflammatory myopathy (IIM) against healthy controls, and also the association of quantitative T2 values in patients with ...

The study of organic molecular crystals under high pressure provides fundamental insight into crystal packing distortions and reveals mechanisms of phase transitions and the crystallization of polymorphs. These solid-state transformations can be monitored ...

Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit ...

The growth of data throughput in optical microscopy has triggered the extensive use of supervised learning (SL) models on compressed datasets for automated analysis. Investigating the effects of image compression on SL predictions is therefore pivotal to a ...

Supercritical water is used in a variety of chemical and industrial applications. As a consequence, a detailed knowledge of the structure-properties correlations is of uttermost importance. Although supercritical water was considered as a homogeneous fluid ...

Water activity and related thermodynamic properties are calculated for several aqueous solutions using equilibrium molecular dynamics in conjunction with the recent extension of the Kirkwood-Buff (KB) theory for closed systems. The general applicability of ...

The modeling of non-covalent interactions, solvation effects, and chemical reactions in complex molecular environment is a challenging task. Current state-of-the-art approaches often rely on static computations using implicit solvent models and harmonic ap ...

In this work we investigate how the crystal packing affects the spin crossover (SCO) of [Fe-II(2-pic)(3)]Cl-2. Four alcohol solvatomorphs of this compound have been reported to present a transition from a singlet low spin (LS, S = 0) to a quintet high spin ...

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environme ...

Background: Healthy older adults show a decrease in motor performance and motor learning capacity as well as in working memory (WM) performance. WM has been suggested to be involved in motor learning processes, such as sequence learning. Correlational evid ...

Data from animal-borne inertial sensors are widely used to investigate several aspects of an animal's life, such as energy expenditure, daily activity patterns and behaviour. Accelerometer data used in conjunction with machine learning algorithms have b ...

Molecular electronics covers several distinctly different conducting architectures, including organic semiconductors and single-molecule junctions. The noncovalent interactions, abundant in the former, are also often found in the latter, i.e., the dimer ju ...

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

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

Machine learning for metallurgy II. A neural-network potential for magnesium

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol

Machine learning for metallurgy I. A neural-network potential for Al-Cu

Accurate molecular polarizabilities with coupled cluster theory and machine learning

Photocatalytic system comprising a titanium-based mof

Effects of surface rigidity and metallicity on dielectric properties and ion interactions at aqueous hydrophobic interfaces

Unsupervised machine learning in atomistic simulations, between predictions and understanding

Assessment of idiopathic inflammatory myopathy using a deep learning method for muscle T2 mapping segmentation

DORI Reveals the Influence of Noncovalent Interactions on Covalent Bonding Patterns in Molecular Crystals Under Pressure

Beyond potentials: Integrated machine learning models for materials

Statistical distortion of supervised learning predictions in optical microscopy induced by image compression

Supercritical water anomalies in the vicinity of the Widom line

Water Activity from Equilibrium Molecular Dynamics Simulations and Kirkwood-Buff Theory

Modeling non-covalent interactions in condensed phase

Deciphering crystal packing effects in the spin crossover of six [Fe-II(2-pic)(3)]Cl-2 solvatomorphs

Assessing the persistence of chalcogen bonds in solution with neural network potentials

Targeting the frontoparietal network using bifocal transcranial alternating current stimulation during a motor sequence learning task in healthy older adults

A novel biomechanical approach for animal behaviour recognition using accelerometers

Noncovalent Molecular Electronics