Publication

Machine Learning-Guided Approach for Studying Solvation Environments

Publications associées (31)

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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 ...

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Effects of surface wettability on (001)-WO3 and (100)-WSe2: A spin-polarized DFT-MD study

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An extensive understanding of WO3 and WSe2 bulk crystalline structures and explicit solvent effects on (001)-WO3 and (100)-WSe2 facets are essential for design of efficient (photo) electrocatalysts. The atomistic level understanding of both WO(3 )and WSe2 ...

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

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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 ...

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High-Throughput Nanopore Fabrication and Classification Using Xe-Ion Irradiation and Automated Pore-Edge Analysis

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Large-area nanopore drilling is a major bottleneck in state-of-the-art nanoporous 2D membrane fabrication protocols. In addition, high-quality structural and statistical descriptions of as-fabricated porous membranes are key to predicting the corresponding ...

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

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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 ...

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Computational design of novel protein–protein interactions – An overview on methodological approaches and applications

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Protein–protein interactions (PPIs) govern numerous cellular functions in terms of signaling, transport, defense and many others. Designing novel PPIs poses a fundamental challenge to our understanding of molecular interactions. The capability to robustly ...

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Transferable machine-learning models of complex materials: the case of GaAs

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Molecular simulations allow to investigate the behaviour of materials at the atomistic level, shedding light on phenomena that cannot be directly observed in experiments. Accurate results can be obtained with ab initio methods, while simulations of large-s ...

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A general and efficient framework for atomistic machine learning

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Over the last two decades, many technological and scientific discoveries, ranging from the development of materials for energy conversion and storage through the design of new drugs, have been accelerated by the use of preliminary in silico experiments, to ...

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Rapid screening of nanopore candidates in nanoporous single-layer graphene for selective separations using molecular visualization and interatomic potentials

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Nanoporous single-layer graphene is promising as an ideal membrane because of its extreme thinness, chemical resistance, and mechanical strength, provided that selective nanopores are successfully incorporated. However, screening and understanding the tran ...

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Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

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Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm fo ...

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