Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry

AS Anker, KT Butler, R Selvan, KMØ Jensen - Chemical Science, 2023 - pubs.rsc.org
The rapid growth of materials chemistry data, driven by advancements in large-scale
radiation facilities as well as laboratory instruments, has outpaced conventional data …

Capturing dynamical correlations using implicit neural representations

SR Chitturi, Z Ji, AN Petsch, C Peng, Z Chen… - Nature …, 2023 - nature.com
Understanding the nature and origin of collective excitations in materials is of fundamental
importance for unraveling the underlying physics of a many-body system. Excitation spectra …

[HTML][HTML] Update of HΦ: Newly added functions and methods in versions 2 and 3

K Ido, M Kawamura, Y Motoyama, K Yoshimi… - Computer Physics …, 2024 - Elsevier
H Φ [aitch-phi] is an open-source software package of numerically exact and stochastic
calculations for a wide range of quantum many-body systems. In this paper, we present the …

On ultrafast x-ray scattering methods for magnetism

R Plumley, SR Chitturi, C Peng, TA Assefa… - … in Physics: X, 2024 - Taylor & Francis
With the introduction of x-ray free electron laser sources around the world, new scientific
approaches for visualizing matter at fundamental length and time-scales have become …

Engineering the Kitaev spin liquid in a quantum dot system

T Cookmeyer, S Das Sarma - Physical Review Letters, 2024 - APS
The Kitaev model on a honeycomb lattice may provide a robust topological quantum
memory platform, but finding a material that realizes the unique spin-liquid phase remains a …

Direct prediction of inelastic neutron scattering spectra from the crystal structure

Y Cheng, G Wu, DM Pajerowski… - Machine Learning …, 2023 - iopscience.iop.org
Inelastic neutron scattering (INS) is a powerful technique to study vibrational dynamics of
materials with several unique advantages. However, analysis and interpretation of INS …

A perspective on machine learning and data science for strongly correlated electron problems

S Johnston, E Khatami, R Scalettar - Carbon Trends, 2022 - Elsevier
Numerical approaches to the correlated electron problem have achieved considerable
success, yet are still constrained by several bottlenecks, including high order polynomial or …

Thermal spin dynamics of Kitaev magnets: Scattering continua and magnetic field induced phases within a stochastic semiclassical approach

O Franke, D Călugăru, A Nunnenkamp, J Knolle - Physical Review B, 2022 - APS
The honeycomb magnet α-RuCl 3 is a prime candidate material for realizing the Kitaev
quantum spin liquid (QSL), but it shows long-range magnetic order at low temperature …

Simulations of frustrated Ising Hamiltonians using quantum approximate optimization

PC Lotshaw, H Xu, B Khalid… - … of the Royal …, 2023 - royalsocietypublishing.org
Novel magnetic materials are important for future technological advances. Theoretical and
numerical calculations of ground-state properties are essential in understanding these …

Kernel polynomial method for linear spin wave theory

H Lane, H Zhang, D Dahlbom, S Quinn, R Somma… - SciPost Physics, 2024 - scipost.org
Calculating dynamical spin correlations is essential for matching model magnetic exchange
Hamiltonians to momentum-resolved spectroscopic measurements. A major numerical …