Publications related to Energy profile (chemistry)

Weakly nonlinear versus semi-linear models of the nonlinear evolution of the centrifugal instability

We carry out a weakly nonlinear analysis of the centrifugal instability for a columnar vortex in a rotating fluid, and compare the results to those of the semi-linear model derived empirically by Yim et al. (J. Fluid Mech., vol. 897, 2020, A34). The asympt ...

CAMBRIDGE UNIV PRESS2023

Inferring free-energy barriers and kinetic rates from molecular dynamics via underdamped Langevin models

Sara Bonella, Fabio Pietrucci, David Daniel Girardier

Rare events include many of the most interesting transformation processes in condensed matter, from phase transitions to biomolecular conformational changes to chemical reactions. Access to the corresponding mechanisms, free-energy landscapes and kinetic r ...

Melville2023

How Robust Is the Reversible Steric Shielding Strategy for Photoswitchable Organocatalysts?

Simone Gallarati, Raimon Fabregat I De Aguilar-Amat, Veronika Juraskova, Théo Pierre Jaffrelot Inizan

A highly appealing strategy to modulate a catalyst's activity and/or selectivity in a dynamic and noninvasive way is to incorporate a photoresponsive unit into a catalytically competent molecule. However, the description of the photoinduced conformational ...

AMER CHEMICAL SOC2022

Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry

Alberto Fabrizio, Benjamin André René Meyer, Raimon Fabregat I De Aguilar-Amat, Daniel Hollas

This work combines a machine learning potential energy function with a modular enhanced sampling scheme to obtain statistically converged thermodynamical properties of flexible medium-size organic molecules at high ab initio level. We offer a modular envir ...

AMER CHEMICAL SOC2020

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Michele Ceriotti, Fabio Pietrucci

Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from t ...

AMER CHEMICAL SOC2020

Balancing Density Functional Theory Interaction Energies in Charged Dimers Precursors to Organic Semiconductors

Alberto Fabrizio, Riccardo Petraglia

The pursuit of an increasingly accurate description of intermolecular interactions within the framework of Kohn-Sham density functional theory (KS-DFT) has motivated the construction of numerous benchmark databases over the past two decades. By far, the la ...

AMER CHEMICAL SOC2020

Efficient Kr/Xe separation from triangular g-C(3)N(4)nanopores, a simulation study

Nicola Marzari, Kumar Varoon Agrawal, Davide Campi, Nicola Colonna, Luis Francisco Villalobos Vazquez de la Parra

Poly(triazine imide) or PTI is a promising material for molecular sieving membranes, thanks to its atom-thick ordered lattice with an extremely high density (1.6 x 10(14)pores per cm(2)) of triangular-shaped nanopores of similar to 0.34 nm diameter. Here, ...

2020

Updated ATLAS of Biochemistry with New Metabolites and Improved Enzyme Prediction Power

Vassily Hatzimanikatis, Jasmin Maria Hafner, Anastasia Sveshnikova, Alan Scheidegger

The ATLAS of Biochemistry is a repository of both known and novel predicted biochemical reactions between biological compounds listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG). ATLAS was originally compiled based on KEGG 2015, though the number ...

AMER CHEMICAL SOC2020

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Energy profile (chemistry)

Weakly nonlinear versus semi-linear models of the nonlinear evolution of the centrifugal instability

Inferring free-energy barriers and kinetic rates from molecular dynamics via underdamped Langevin models

How Robust Is the Reversible Steric Shielding Strategy for Photoswitchable Organocatalysts?

Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Balancing Density Functional Theory Interaction Energies in Charged Dimers Precursors to Organic Semiconductors

Efficient Kr/Xe separation from triangular g-C(3)N(4)nanopores, a simulation study

On-the-fly ab initio semiclassical evaluation of third-order response functions for two-dimensional electronic spectroscopy

Updated ATLAS of Biochemistry with New Metabolites and Improved Enzyme Prediction Power

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Chat with Graph Search

How Robust Is the Reversible Steric Shielding Strategy for Photoswitchable Organocatalysts?

Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Balancing Density Functional Theory Interaction Energies in Charged Dimers Precursors to Organic Semiconductors

On-the-fly ab initio semiclassical evaluation of third-order response functions for two-dimensional electronic spectroscopy

Inferring free-energy barriers and kinetic rates from molecular dynamics via underdamped Langevin models

Updated ATLAS of Biochemistry with New Metabolites and Improved Enzyme Prediction Power

Efficient Kr/Xe separation from triangular g-C(3)N(4)nanopores, a simulation study