Related papers: A "Magnetic" Machine Learning Interatomic Potentia…
We study the nature of spin excitations of individual transition metal atoms (Ti, V, Cr, Mn, Fe, Co and Ni) deposited on a Cu$_2$N/Cu(100) surface using both spin-polarized density functional theory (DFT) and exact diagonalization of an…
A computational approach combining dispersion-corrected density functional theory (DFT) and classical molecular dynamics is employed to characterize the geometrical and thermo-mechanical properties of a recently proposed 2D transition metal…
Choice of appropriate force field is one of the main concerns of any atomistic simulation that needs to be seriously considered in order to yield reliable results. Since, investigations on mechanical behavior of materials at micro/nanoscale…
The physical characterisation and understanding of molecular magnetic materials is one of the most important steps towards the integration of such systems in hybrid spintronic devices. Amongst the many characterisation techniques employed…
We developed a method for fitting machine-learning interatomic potentials with magnetic degrees of freedom, namely, magnetic Moment Tensor Potentials (mMTP). The main feature of our method consists in fitting mMTP to magnetic forces…
The mechanical and thermal performance of high-entropy ceramics are critical to their use in extreme conditions. However, the vast composition space of high-entropy ceramic significantly hinders their development with desired mechanical and…
One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as the density functional theory (DFT)…
Magnetic nanoparticles are important building blocks for future technologies ranging from nano-medicine to spintronics. Many related applications require nanoparticles with tailored magnetic properties. However, despite significant efforts…
Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformations remains difficult. We present error metrics and…
At the heart of the flourishing field of machine learning potentials are graph neural networks, where deep learning is interwoven with physics-informed machine learning (PIML) architectures. Various PIML models, upon training with density…
Metals are traditionally considered hard matter. However, it is well known that their atomic lattices may become dynamic and undergo reconfigurations even well-below the melting temperature. The innate atomic dynamics of metals is directly…
We present an automated benchmarking suite for face-centered-cubic (FCC) nickel that evaluates 47 quantitative metrics spanning both standard tests (equation of state, elastic constants, surface energies and phonons) and…
Over the last two decades magic angle spinning dynamic nuclear polarization (MAS DNP) has revolutionized NMR for materials characterization, tackling its main limitation of intrinsically low sensitivity. Progress in theoretical…
Machine Learning Interatomic Potentials (MLIPs) are a modern computational method that allows achieving near-quantum mechanical accuracy (DFT) while still describing large-scale systems in molecular dynamics (MD) simulations. In this work,…
To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied;…
We use density functional theory to study the structural, magnetic and electronic structure of the organo-metallic quantum magnet $\mathrm{NiCl_2-4SC(NH_2)_2}$ (DTN). Recent work has demonstrated the quasi-1D nature of the molecular crystal…
Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems with ab initio accuracy. Still, the community awaits universal…
A machine learning-accelerated high-throughput (HTP) workflow for the discovery of magnetic materials is presented. As a test case, we screened quaternary and all-$d$ Heusler compounds for stable compounds with large magnetocrystalline…
We present a general framework for modeling power magnetic materials characteristics using deep neural networks. Magnetic materials represented by multidimensional characteristics (that mimic measurements) are used to train the neural…
Density-functional theory with extended Hubbard functionals (DFT+$U$+$V$) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction…