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Proposing genotoxicity related quantitative indicators by analyzing TiO2 surf...
This dataset houses a research poster and its poster abstract. The set of documents was first presented at the doctoral days organized by the Doctoral Committee of the... -
Replication Data for: Prediction of Electronic Density of States in Guanine-T...
This dataset houses the code and data related to the paper titled "Prediction of Electronic Density of States in Guanine-TiO2 Adsorption Model based on Machine Learning.”... -
Intrinsically disordered region of talin’s FERM domain functions as an initia...
Focal adhesions (FAs) mediate the interaction of the cytoskeleton with the extracellular matrix (ECM) in a highly dynamic fashion. talin is a central regulator, adaptor protein... -
Transport coefficients from equilibrium molecular dynamics
The determination of transport coefficients through the time-honoured Green-Kubo theory of linear response and equilibrium molecular dynamics requires significantly longer... -
Electrolyte design for reversible zinc metal chemistry
Here we present an on-demand strategy for electrolytes design to surpass 99.9% Coulombic efficiency (CE) in zinc metal anode. This strategy synergizes various effects by... -
Crystallization kinetics in Ge-rich Ge<sub>x</sub>Te alloys from large scale ...
A machine-learned interatomic potential for Ge-rich GexTe alloys has been developed aiming at uncovering the kinetics of phase separation and crystallization in these materials.... -
Structural transitions of calcium carbonate by molecular dynamics simulation
Calcium carbonate (CaCO₃) plays a crucial role in the global carbon cycle, and its phase diagram is of significant scientific interest. We used molecular dynamics to investigate... -
Structural transitions of calcium carbonate by molecular dynamics simulation
Calcium carbonate (CaCO₃) plays a crucial role in the global carbon cycle, and its phase diagram is of significant scientific interest. We used molecular dynamics to investigate... -
A dual-cutoff machine-learned potential for condensed organic systems obtaine...
Machine-learned potentials (MLPs) trained on ab initio data combine the computational efficiency of classical interatomic potentials with the accuracy and generality of the... -
Nuclear quantum effects on the electronic structure of water and ice
The electronic properties and optical response of ice and water are intricately shaped by their molecular structure, including the quantum mechanical nature of hydrogen atoms.... -
Solvation free energies from machine learning molecular dynamics
In this paper, we propose an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles... -
Dynamics of the charge transfer to solvent process in aqueous iodide
Charge-transfer-to-solvent states in aqueous halides are ideal systems for studying the electron-transfer dynamics to the solvent involving a complex interplay between... -
Mechanism of charge transport in lithium thiophosphate
Lithium ortho-thiophosphate (Li₃PS₄) has emerged as a promising candidate for solid-state-electrolyte batteries, thanks to its highly conductive phases, cheap components, and... -
Mechanism of charge transport in lithium thiophosphate
Lithium ortho-thiophosphate (Li₃PS₄) has emerged as a promising candidate for solid-state-electrolyte batteries, thanks to its highly conductive phases, cheap components, and... -
Intrinsically disordered region of talin’s FERM domain functions as an initia...
Focal adhesions (FAs) mediate the interaction of the cytoskeleton with the extracellular matrix (ECM) in a highly dynamic fashion. talin is a central regulator, adaptor protein... -
Reduction of interlayer interaction in multilayer stacking graphene with carb...
We insert carbon nanotubes (CNT) as nanospacers to modulate the microstructure of multilayer stacking graphene. Nanospacers can increase interlayer distance and reduce... -
Conformational Investigations in Flexible Molecules using Orientational NMR C...
This dataset has no description
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Machine learning of twin/matrix interfaces from local stress field
Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic... -
The optimal resolution level of a protein is an emergent property of its stru...
Molecular dynamics simulations provide a wealth of data whose in-depth analysis can be computationally demanding and, sometimes, even unnecessary. Dimensionality reduction... -
Developments and further applications of ephemeral data derived potentials
Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP),...