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Top Papers in Ab initio random structure searching

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Ab initio random structure searching for battery cathode materials

Cathodes are critical components of rechargeable batteries. Conventionally,
the search for cathode materials relies on experimental trial-and-error and a
traversing of existing computational/experimen

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Ab-initio random structure searching for low-cost cathode materials in Li-ion batteries

Accelerating Cathode Material Discovery through Ab Initio Random Structure Searching

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Ground-State Structures of Ice at High-Pressures

\textit{Ab initio} random structure searching based on density functional
theory is used to determine the ground-state structures of ice at high
pressures. Including estimates of lattice zero-point en

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Ephemeral data derived potentials for random structure search

Structure prediction has become a key task of the modern atomistic sciences,
and depends on the rapid and reliable computation of the energy landscape.
First principles density functional based calcul

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Crystal Structure Search with Random Relaxations Using Graph Networks

Materials design enables technologies critical to humanity, including
combating climate change with solar cells and
batteries\cite{tabor_accelerating_2018, sendek_machine_2019,
jacobs_materials_2019}.

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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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Application of the quantum algorithm of adiabatic state preparation with quantum phase estimation in ab initio nuclear structure theory

Ab initio nuclear structure via quantum adiabatic algorithm

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A Transferable High-fidelity Hamiltonian Representation of Electronic Structure in Ab Initio Molecular Dynamics

Neural network representation of electronic structure from ab initio molecular dynamics

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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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Energy-free machine learning predictions of {\em ab initio} structures

The computational prediction of atomistic structure is a long-standing
problem in physics, chemistry, materials, and biology. Within conventional
force-field or {\em ab initio} calculations, structure

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An ab initio derivation method for effective low-energy Hamiltonians of material with strong spin-orbit interactions

Ab initio Derivation of Low-Energy Hamiltonians for Systems with Strong Spin-Orbit Interaction and Its Application to Ca5Ir3O12

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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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Thermodynamic, structural, and transport properties of dense carbon up to 10 million Kelvin from Kohn-Sham density functional theory calculations

Accurately modeling dense plasmas over wide ranging conditions of pressures
and temperatures is a grand challenge critically important to our understanding
of stellar and planetary physics as well as

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AlphaCrystal: Neural Network Based Ab Initio Crystal Structure Prediction

AlphaCrystal: Contact map based crystal structure prediction using deep learning

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Highly Efficient and Accurate ab initio Bayesian Active Learning Method for Accelerating Discovery of Advanced Functional Materials

Discovery of advanced materials with conventional trial-and-error method
usually suffers tremendous difficulties. Machine learning offers a great
opportunity to discover target materials, but unbalanc

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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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Predictive Models of Hamiltonian and Overlap Matrixes from Atomic Cluster Expansion Data

Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models

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Prediction of stable Li-Sn compounds: boosting ab initio searches with neural network potentials

The Li-Sn binary system has been the focus of extensive research because it
features Li-rich alloys with potential applications as battery anodes. Our
present re-examination of the binary system with

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Neural Network Potentials for Reactive Simulations

Accurate large-scale simulations of siliceous zeolites by neural network potentials

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Deep-neural-network solution of the ab initio nuclear structure

Predicting the structure of quantum many-body systems from the first
principles of quantum mechanics is a common challenge in physics, chemistry,
and material science. Deep machine learning has proven

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