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Molecular-level understanding and characterization of solvation environments are often needed across chemistry, biology, and engineering. Toward practical modeling of local solvation effects of any solute in any solvent, we report a static and all-quantum ...

Machine learning promises to accelerate materials discovery by allowing computational efficient property predictions from a small number of reference calculations. As a result, the literature has spent a considerable effort in designing representations tha ...

In this study, the authors investigate the impact of radical oxygen plasma on nitrided and annealed atomic layer deposited (ALD) SiO2 as a thick gate oxide (1.65-3 V) with a high-k/metal gate transistor. Time-dependent-dielectric-breakdown voltage, seconda ...

Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole-based azaphenacene molecule (pyri ...

Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurations. Many approaches to the construction of such fingerprints, also called ...

Due to their strong dependence on local atonic environments, NMR chemical shifts are among the most powerful tools for strucutre elucidation of powdered solids or amorphous materials. Unfortunately, using them for structure determination depends on the abi ...

Evaluating the (dis)similarity of crystalline, disordered and molecular compounds is a critical step in the development of algorithms to navigate automatically the configuration space of complex materials. For instance, a structural similarity metric is cr ...

Determining the stability ofmolecules and condensed phases is the cornerstone of atomisticmodeling, underpinning our understanding of chemical andmaterials properties and transformations. We show that amachine-learningmodel, based on a local description of ...

High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from computational search ...

Machine learning promises to accelerate materials discovery by allowing computational efficient property predictions from a small number of reference calculations. As a result, the literature spent a considerable effort in designing representations that ca ...

Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in cat ...

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Machine Learning-Guided Approach for Studying Solvation Environments

Chemical machine learning with kernels: The impact of loss functions

Impact of oxygen plasma on nitrided and annealed atomic layer deposited SiO2/high-k/metal gate for high-voltage input and output fin-shaped field effect transistor devices

Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

An assessment of the structural resolution of various fingerprints commonly used in machine learning

Chemical shifts in molecular solids by machine learning

Comparing molecules and solids across structural and alchemical space

Machine learning unifies the modeling of materials and molecules

Mapping and classifying molecules from a high-throughput structural database

Chemical machine learning with kernels: The key impact of loss functions

Modeling high-entropy transition metal alloys with alchemical compression

Applications of machine learning for studying local solvation environments