Saved in:
| Main Authors: | Yu, Qi, Ma, Ruitao, Qu, Chen, Conte, Riccardo, Nandi, Apurba, Pandey, Priyanka, Houston, Paul L., Zhang, Dong H., Bowman, Joel M. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.00522 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials
by: Houston, Paul L., et al.
Published: (2024)
by: Houston, Paul L., et al.
Published: (2024)
The quantum nature of ubiquitous vibrational features revealed for ethylene glycol
by: Nandi, Apurba, et al.
Published: (2025)
by: Nandi, Apurba, et al.
Published: (2025)
A $Δ$-Machine Learning Approach for Force Fields, Illustrated by a CCSD(T) 4-body Correction to the MB-pol Water Potential
by: Qu, Chen, et al.
Published: (2022)
by: Qu, Chen, et al.
Published: (2022)
Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods
by: Houston, Paul L., et al.
Published: (2021)
by: Houston, Paul L., et al.
Published: (2021)
$Δ$-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol
by: Nandi, Apurba, et al.
Published: (2024)
by: Nandi, Apurba, et al.
Published: (2024)
Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
by: Ge, Fuchun, et al.
Published: (2024)
by: Ge, Fuchun, et al.
Published: (2024)
Observation of non-Hermitian many-body phase transition in a Rydberg-atom array
by: Zhang, Yao-Wen, et al.
Published: (2025)
by: Zhang, Yao-Wen, et al.
Published: (2025)
Direct three-body atom recombination: halogen atoms
by: Koots, Rian, et al.
Published: (2025)
by: Koots, Rian, et al.
Published: (2025)
"Gold-Standard" $Δ$-Machine Learned and Transferable Potential for Linear Alkanes
by: Qu, Chen, et al.
Published: (2025)
by: Qu, Chen, et al.
Published: (2025)
Recent advancements in atomic many-body methods for high-precision studies of isotope shifts
by: Sahoo, B. K., et al.
Published: (2024)
by: Sahoo, B. K., et al.
Published: (2024)
Ground-state selection via many-body superradiant decay
by: Mok, Wai-Keong, et al.
Published: (2024)
by: Mok, Wai-Keong, et al.
Published: (2024)
Superradiant active optical atomic clocks: motivations and current challenges
by: Matusko, Martina, et al.
Published: (2024)
by: Matusko, Martina, et al.
Published: (2024)
High-precision measurement of microwave electric field by cavity-enhanced critical behavior in a many-body Rydberg atomic system
by: Wang, Qinxia, et al.
Published: (2025)
by: Wang, Qinxia, et al.
Published: (2025)
Ultracold charged atom-dimer collisions: state-selective charge exchange and three-body recombination
by: Pandey, Amrendra, et al.
Published: (2024)
by: Pandey, Amrendra, et al.
Published: (2024)
Vacuum polarization corrections to hyperfine structure in many-electron atoms
by: Hasted, J. C., et al.
Published: (2024)
by: Hasted, J. C., et al.
Published: (2024)
Extending the fundamental limit of atomic clock stability
by: Shaniv, Ravid, et al.
Published: (2026)
by: Shaniv, Ravid, et al.
Published: (2026)
Entanglement witness for combined atom interferometer-mechanical oscillator setup
by: Premawardhana, Gayathrini, et al.
Published: (2025)
by: Premawardhana, Gayathrini, et al.
Published: (2025)
Simultaneous nondestructive measurement of many polar molecules using Rydberg atoms
by: Young, Jeremy T., et al.
Published: (2026)
by: Young, Jeremy T., et al.
Published: (2026)
On the global minimum of the classical potential energy for clusters bound by many-body forces
by: Kiessling, Michael K. -H., et al.
Published: (2023)
by: Kiessling, Michael K. -H., et al.
Published: (2023)
Zeptosecond to attosecond dynamics in atoms and possibility of generating a zeptosecond light source
by: Nandi, T., et al.
Published: (2025)
by: Nandi, T., et al.
Published: (2025)
Bremsstrahlung induced atomic processes
by: Singh, Shashank, et al.
Published: (2025)
by: Singh, Shashank, et al.
Published: (2025)
VPT2 Calculations of Vibrational Energies of CH3COOC6H4COOH Done in Seconds on a Laptop Using a Machine Learned Potential
by: Kotaru, Saikiran, et al.
Published: (2026)
by: Kotaru, Saikiran, et al.
Published: (2026)
Trapping potentials and quantum gates for microwave-dressed Rydberg atoms on an atom chip
by: Tsiamis, Iason, et al.
Published: (2025)
by: Tsiamis, Iason, et al.
Published: (2025)
Nondestructive characterization of laser-cooled atoms using machine learning
by: De Sousa, G., et al.
Published: (2025)
by: De Sousa, G., et al.
Published: (2025)
An optically accelerated extreme learning machine using hot atomic vapors
by: Azam, Pierre, et al.
Published: (2024)
by: Azam, Pierre, et al.
Published: (2024)
Time series learning in a many-body Rydberg system with emergent collective nonlinearity
by: Liu, Zongkai, et al.
Published: (2025)
by: Liu, Zongkai, et al.
Published: (2025)
Double interatomic Coulombic electron capture induced by a single incident electron in two- and three-center atomic systems
by: Ellerbrock, L. M., et al.
Published: (2024)
by: Ellerbrock, L. M., et al.
Published: (2024)
Multipole decomposition of the thermal one-loop self-energy correction for a bound atomic electron
by: Lopez-Rodriguez, J. J., et al.
Published: (2025)
by: Lopez-Rodriguez, J. J., et al.
Published: (2025)
Collective light shifts of many longitudinal cavity modes induced by coupling to a cold-atom ensemble
by: Ðujić, Marin, et al.
Published: (2025)
by: Ðujić, Marin, et al.
Published: (2025)
A novel method for measuring the Fermi velocity of elemental targets
by: Kaur, Manpreet, et al.
Published: (2025)
by: Kaur, Manpreet, et al.
Published: (2025)
Interaction-assisted topological pumping in few- and many-atom Rydberg arrays
by: Huang, Chenxi, et al.
Published: (2025)
by: Huang, Chenxi, et al.
Published: (2025)
Electron Impact Fragmentation Dynamics of Carbonyl Sulfide: A Combined Experimental and Theoretical Study
by: Ghosh, Soumya, et al.
Published: (2025)
by: Ghosh, Soumya, et al.
Published: (2025)
Study of low-energy electron-induced dissociation of 1-Propanol
by: Ghosh, Soumya, et al.
Published: (2025)
by: Ghosh, Soumya, et al.
Published: (2025)
Role of spontaneously generated coherence (SGC) in laser cooling of atoms
by: Das, Rajnandan Choudhury, et al.
Published: (2024)
by: Das, Rajnandan Choudhury, et al.
Published: (2024)
Assessing PIP and sGDML Potential Energy Surfaces for H3O2-
by: Pandey, Priyanka, et al.
Published: (2024)
by: Pandey, Priyanka, et al.
Published: (2024)
Imaging atomic scattering potential in centroidal diffraction of elastic electrons
by: Aiswarya, R., et al.
Published: (2025)
by: Aiswarya, R., et al.
Published: (2025)
Energy-scaling of the product state distribution for three-body recombination of ultracold atoms
by: Haze, Shinsuke, et al.
Published: (2022)
by: Haze, Shinsuke, et al.
Published: (2022)
Spin-conservation propensity rule for three-body recombination of ultracold Rb atoms
by: Haze, Shinsuke, et al.
Published: (2021)
by: Haze, Shinsuke, et al.
Published: (2021)
Reconfigurable dissipative entanglement between many spin ensembles: from robust quantum sensing to many-body state engineering
by: Chu, Anjun, et al.
Published: (2025)
by: Chu, Anjun, et al.
Published: (2025)
Direct three body dynamics govern ion atom recombination and barrierless termolecular reactions
by: Koots, Rian, et al.
Published: (2026)
by: Koots, Rian, et al.
Published: (2026)
Similar Items
-
No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials
by: Houston, Paul L., et al.
Published: (2024) -
The quantum nature of ubiquitous vibrational features revealed for ethylene glycol
by: Nandi, Apurba, et al.
Published: (2025) -
A $Δ$-Machine Learning Approach for Force Fields, Illustrated by a CCSD(T) 4-body Correction to the MB-pol Water Potential
by: Qu, Chen, et al.
Published: (2022) -
Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods
by: Houston, Paul L., et al.
Published: (2021) -
$Δ$-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol
by: Nandi, Apurba, et al.
Published: (2024)