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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

We demonstrate that ab-initio random structure searching (airss), a first-principles structure prediction methods that doesnot rely on any pre-existing data, can locate low energy structures of complex cathode materials efficiently based only on chemical composition.

We use airss to explore three fe-containing polyanion compounds as low-cost cathodes, predicting a range of redox-active phases that have yet to be experimentally synthesized, demonstrating the suitability of airss as a tool for accelerating the discovery of novel cathode materials.

Earth and Planetary Astrophysics

Other Condensed Matter

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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Machine Learning

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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Earth and Planetary Astrophysics

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

Nuclear Theory

Quantum Physics

Ab initio nuclear structure via quantum adiabatic algorithm

We explore the application of the quantum algorithm of adiabatic state preparation with quantum phase estimation in ab initio nuclear structure theory.

We illustrate this algorithm by solving the deuteron ground state energy and the spectrum of the deuteron bounded in a harmonic oscillator trapimplementing the ibm qiskit quantum simulator.

Disordered Systems and Neural Networks

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

We introduce a transferable high-fidelity neural networkrepresentation of ab initio electronic structure data in the form of tight-binding tight-binding hamiltonians for crystalline materials.

This predictive representation of ab initio electronic structure, combined with machine-learning boosted molecular dynamics, enables efficient and accurate electronic evolution and sampling.

Machine Learning

Neural and Evolutionary Computing

Modeling Interatomic Potentials with Symbolic Regression and Genetic Programming

Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data

This contribution discusses the role of symbolic regression in materials science and offers a comprehensive overview of current methodological challenges and state-of-the-art results.

A genetic programming-based approach for modeling atomic potentials from raw data (consisting of snapshots of atomicpositions and associated potential energy) is presented and empirically validated on ab initio electronic structure data.

Chemical Physics

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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Strongly Correlated Electrons

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

We present an ab initio derivation method for effective low-energy low-energy electronic structures of material with strong spin-orbit interactions.

Based on this formalism and the developed code, we derive an effective low-energy electronic structure of a strong spin-orbit interaction material, which consists of three edge-shared edge-shared iro6 octahedral chains arranged along the c axis, and the three ir atoms in the ab plane compose a triangular lattice.

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Chemical Physics

Machine Learning for Equilibrium Structures in Chemical Compound Space

Ab initio machine learning of phase space averages

We propose to machine learn phase-space averages, conventionally obtained by \textit(ab initio} molecular dynamics or force-field based molecular dynamics (md) or monte carlo simulations.

Our {\em ab initio} machine learning (aiml) model does not require bondtopologies and therefore enables a general machine learning pathway to ensembleproperties throughout chemical compound space (ccs) at a much accelerated pace.

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

Plasma Physics

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

We propose a deep residual neural network model that learns deep knowledge to guide predicting the atomic contact map of a target crystal material followed by reconstructing its 3-dimensional (3d) crystal structure using genetic algorithms.

It can also speed up the crystal structure prediction process by predicting and exploiting the predicted contact map so that it has the potential to handle relatively large systems.

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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Chemical Physics

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

We propose a data-driven scheme to construct predictive models of hamiltonianand overlap matrices in atomic orbital representation from ab initio data as a function of local atomic and bond environments.

The approach produces equivariantanalytical maps from first principles data to linear models for the hamiltonianand overlap matrices that transform equivariantly with respect to the full rotation group in 3 dimensions.

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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