Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Kipton, Barros"'
Autor:
Alice E. A. Allen, Nicholas Lubbers, Sakib Matin, Justin Smith, Richard Messerly, Sergei Tretiak, Kipton Barros
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-9 (2024)
Abstract The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable t
Externí odkaz:
https://doaj.org/article/a6d03e20793240fea387226429c6ed76
Autor:
Joseph A. M. Paddison, Hao Zhang, Jiaqiang Yan, Matthew J. Cliffe, Michael A. McGuire, Seung-Hwan Do, Shang Gao, Matthew B. Stone, David Dahlbom, Kipton Barros, Cristian D. Batista, Andrew D. Christianson
Publikováno v:
npj Quantum Materials, Vol 9, Iss 1, Pp 1-8 (2024)
Abstract Magnetic materials with noncoplanar magnetic structures can show unusual physical properties driven by nontrivial topology. Topologically-active states are often multi-q structures, which are challenging to stabilize in models and to identif
Externí odkaz:
https://doaj.org/article/cd162b79c7d34a11b3138af088e0eaf7
Publikováno v:
SciPost Physics, Vol 17, Iss 5, p 145 (2024)
Calculating dynamical spin correlations is essential for matching model magnetic exchange Hamiltonians to momentum-resolved spectroscopic measurements. A major numerical bottleneck is the diagonalization of the dynamical matrix, especially in systems
Externí odkaz:
https://doaj.org/article/d64ade77dede43e281337df0115c5e4b
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-7 (2023)
Abstract Magnetic skyrmions are nanoscale topological textures that have been recently observed in different families of quantum magnets. These objects are called CP1 skyrmions because they are built from dipoles—the target manifold is the 1D compl
Externí odkaz:
https://doaj.org/article/8b301a3d83b64106811091e8552ff27f
Publikováno v:
npj Quantum Materials, Vol 8, Iss 1, Pp 1-9 (2023)
Abstract The Holstein model is a paradigmatic description of the electron-phonon interaction, in which electrons couple to local dispersionless phonon modes, independent of momentum. The model has been shown to host a variety of ordered ground states
Externí odkaz:
https://doaj.org/article/b5ff9644461a489d9192139f6fac80ec
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-9 (2023)
Abstract The relationship between electron–phonon (e-ph) interactions and charge-density-wave (CDW) order in the bismuthate family of high-temperature superconductors remains unresolved. We address this question using nonperturbative hybrid Monte C
Externí odkaz:
https://doaj.org/article/848878272a3149df91038d08d2bcbb7e
Autor:
Seung-Hwan Do, Hao Zhang, David A. Dahlbom, Travis J. Williams, V. Ovidiu Garlea, Tao Hong, Tae-Hwan Jang, Sang-Wook Cheong, Jae-Hoon Park, Kipton Barros, Cristian D. Batista, Andrew D. Christianson
Publikováno v:
npj Quantum Materials, Vol 8, Iss 1, Pp 1-6 (2023)
Abstract Quantum magnets admit more than one classical limit and N-level systems with strong single-ion anisotropy are expected to be described by a classical approximation based on SU(N) coherent states. Here we test this hypothesis by modeling fini
Externí odkaz:
https://doaj.org/article/525f3856cfcd4f2fa660bb141e367a72
Autor:
Marius S. Frank, Denis G. Artiukhin, Tsung-Han Lee, Yongxin Yao, Kipton Barros, Ove Christiansen, Nicola Lanatà
Publikováno v:
Physical Review Research, Vol 6, Iss 1, p 013242 (2024)
Quantum embedding (QE) methods such as the ghost Gutzwiller approximation (gGA) offer a powerful approach to simulating strongly correlated systems, but come with the computational bottleneck of computing the ground state of an auxiliary embedding Ha
Externí odkaz:
https://doaj.org/article/31f83d3e2b9c4728b90e3bbe749a28f7
Autor:
Justin S. Smith, Benjamin Nebgen, Nithin Mathew, Jie Chen, Nicholas Lubbers, Leonid Burakovsky, Sergei Tretiak, Hai Ah Nam, Timothy Germann, Saryu Fensin, Kipton Barros
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
The accuracy of a machine-learned potential is limited by the quality and diversity of the training dataset. Here the authors propose an active learning approach to automatically construct general purpose machine-learning potentials here demonstrated
Externí odkaz:
https://doaj.org/article/f563b56affa04410a543a78699a94661
Autor:
Anjana M. Samarakoon, Kipton Barros, Ying Wai Li, Markus Eisenbach, Qiang Zhang, Feng Ye, V. Sharma, Z. L. Dun, Haidong Zhou, Santiago A. Grigera, Cristian D. Batista, D. Alan Tennant
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
Developing an understanding of a material’s magnetic behaviour based on neutron scattering measurements often relies on extracting an effective spin model. Samarakoon et al. demonstrate an automated machine learning approach to this problem, leadin
Externí odkaz:
https://doaj.org/article/5964d7ee7a0c4c278294454fdb52db06