Related papers: Interatomic potentials for platinum
A general set of methods is presented for calculating chemical potentials in solid and liquid mixtures using {\em ab initio} techniques based on density functional theory (DFT). The methods are designed to give an {\em ab initio} approach…
Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generating databases of atomic configurations used in fitting these models is a laborious process, requiring significant computational and human…
We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, named GAP-20, describes the properties of the bulk crystalline…
Simultaneous monitoring of ac and dc responses of the electrode-electrolyte interface with potentiodynamic electrochemical impedance spectroscopy (PDEIS) in silver underpotential and overpotential deposition on platinum has confirmed the…
We have developed a new machine learned interatomic potential for the prototypical austenitic steel Fe$_{7}$Cr$_{2}$Ni, using the Gaussian approximation potential (GAP) framework. This new GAP can model the alloy's properties with close to…
Accurate prediction of surface energies and stabilities is essential for materials design, yet first-principles calculations remain computationally expensive and most existing interatomic potentials are trained only on bulk systems. Here,…
Machine-learned interatomic potentials (MLPs) provide near density functional theory (DFT) accuracy at reduced computational cost, but their reliability depends on representative training data and often deteriorates in transition-state…
In recent years, Adiabatic Connection Interpolations developed within Density Functional Theory (DFT) have been found to provide satisfactory performances in the calculation of interaction energies when used with Hartree-Fock (HF)…
The accuracy of classical physical property predictions using molecular dynamics simulations is determined by the quality of the interatomic potentials. Here we introduce a training approach for empirical interatomic potentials (EIPs) which…
Noble metals adopt close-packed structures at ambient pressure and rarely undergo structural transformation at high pressures. Platinum (Pt), in particular, is normally considered to be unreactive and is therefore not expected to form…
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is…
We present an analytical description of the Alchemical Transfer Method (ATM) for molecular binding using the Potential Distribution Theory (PDT) formalism. ATM models the binding free energy by mapping the bound and unbound states of the…
One of the most challenging problems in understanding the structural phase transformations in Pu is to determine the energetically favored, continuous atomic pathways from one crystal symmetry to another. This problem involves enumerating…
We present a genetic algorithm (GA) for structural search that combines the speed of structure exploration by classical potentials with the accuracy of density functional theory (DFT) calculations in an adaptive and iterative way. This…
An organometallic powder (platinum (II) acetylacetonate) is decomposed in the post-discharge of an atmospheric RF plasma torch to deposit Pt nanoparticles on carbon black supports. The resulting nanohybrid materials are characterized by…
Materials properties depend strongly on chemical composition, i.e., the relative amounts of each chemical element. Changes in composition lead to entirely different chemical arrangements, which vary in complexity from perfectly ordered…
The error estimation capability of machine learning interatomic potentials (MLIPs) based on probabilistic learning methods such as Gaussian process regression (GPR) is currently under-exploited, because of the tendancy of the predicted…
Time-dependent orbital-free density functional theory (TD-OFDFT) is an efficient ab-initio method for calculating the electronic dynamics of large systems. In comparison to standard TD-DFT, it computes only a single electronic state…
Large language models (LLMs) such as generative pretrained transformers (GPTs) have shown potential for various commercial applications, but their applicability for materials design remains underexplored. In this article, we introduce…
Nanostructures based on platinum, such as small clusters or STM-tips, often exhibit an atomistic structure that relies upon one or very few strongly under-coordinated platinum atoms. Here, we analyze a paradigmatic example, an apex atom on…