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Top Papers in Ab initio

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Benchmarking the ab initio hydrogen equations of state for the interior structure of Jupiter

As Juno is presently measuring Jupiter's gravitational moments to
unprecedented accuracy, models for the interior structure of the planet are
putted to the test. While equations of state based on firs

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Machine Learning for Equilibrium Structures in Chemical Compound Space

Ab initio machine learning of phase space averages

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Nuclear Spectroscopy with Heavy Ion Nucleon Knockout and (p,2p) Reactions

Knockout reactions with heavy ion targets in inverse kinematics, as well as
"quasi-free" (p,2p) and (p,pn) reactions are useful tools for nuclear
spectroscopy. We report calculations on \textit{ab-ini

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Simulations of water and hydrophobic hydration using a neural network potential

Using a neural network potential (ANI-1ccx) generated from quantum data on a
large data set of molecules and pairs of molecules, isothermal, constant volume
simulations demonstrate that the model can

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Ab initio calculation of real solids via neural network ansatz

Neural networks have been applied to tackle many-body electron correlations
for small molecules and physical models in recent years. Here we propose a new
architecture that extends molecular neural ne

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Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method
of choice for modeling complex atomistic phenomena from first principles.
However, most AIMD applications are limited by com

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Electronic Response Quantities of Solids and Deep Learning

We introduce a deep neural network (DNN) framework called the
\textbf{r}eal-space \textbf{a}tomic \textbf{d}ecomposition \textbf{net}work
(\textsc{radnet}), which is capable of making accurate polariz

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The tale of tails: Sensitivity of quasi-free reactions on details of the bound-state overlap functions

It is often stated that one-nucleon knockout in reactions with heavy ion
targets are mostly sensitive to the tails of the bound-state wavefunctions. In
contrast, (p,2p) and (p,pn) reactions are known

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Dialogue on analytical and ab initio methods in attoscience

The perceived dichotomy between analytical and ab initio approaches to theory
in attosecond science is often seen as a source of tension and misconceptions.
This Topical Review compiles the discussion

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Toward ab initio determination of charge symmetry breaking strength of Skyrme functionals

We propose a new approach to determine the strength of the charge symmetry
breaking (CSB) term in the framework of Skyrme density functional theory. It is
shown that once \textit{ab initio} calculatio

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Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations

Molecular dynamics (MD) simulations allow atomistic insights into chemical
and biological processes. Accurate MD simulations require computationally
demanding quantum-mechanical calculations, being pr

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Atomic-Level Simulation of Field Evaporation

Ab-Initio Simulation of Field Evaporation

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First-Principles Study of the Honeycomb-Lattice Iridates Na2IrO3 in the Presence of Strong Spin-Orbit Interaction and Electron Correlations

An effective low-energy Hamiltonian of itinerant electrons for iridium oxide
Na2IrO3 is derived by an ab initio downfolding scheme. The model is then
reduced to an effective spin model on a honeycomb

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Detection of Topological Materials with Machine Learning

Databases compiled using ab-initio and symmetry-based calculations now
contain tens of thousands of topological insulators and topological semimetals.
This makes the application of modern machine lear

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Modeling Interatomic Potentials with Symbolic Regression and Genetic Programming

Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data

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Improved general-purpose five-point model for water: TIP5P/2018

A new five point potential for liquid water, TIP5P/2018, is presented along
with the techniques used to derive its charges from ab initio per-molecule
electrostatic potentials in the liquid phase usin

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Optimal data generation for machine learned interatomic potentials

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

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Systematic electronic structure in the cuprate parent state from quantum many-body simulations

The quantitative description of correlated electron materials remains a
modern computational challenge. We demonstrate a numerical strategy to simulate
correlated materials at the fully ab initio leve

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Phase-Space Ab-Initio Direct and Reverse Ballistic-Electron Emission Spectroscopy: Schottky Barriers Determination for Au/Ge(100)

We develop a phase-space ab-initio formalism to compute Ballistic Electron
Emission Spectroscopy current-voltage I(V)'s in a metal-semiconductor
interface. We consider injection of electrons into the

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Nuclear energy density functionals grounded in ab initio calculations

We discuss the construction of a nuclear Energy Density Functional (EDF) from
ab initio calculations, and we advocate the need of a methodical approach that
is free from ad hoc assumptions. The equati

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