We present an efficient and low-scaling implementation of a density functional theory based method for the computation of electronic g-tensors. It allows for an accurate description of spin-orbit coupling effects by employing the spin-orbit mean-field operator. Gauge-origin independence is ensured by the use of gauge-including atomic orbitals. Asymptotically linear scaling with molecule size is achieved with an atomic orbital based formulation, integral screening methods, and sparse linear algebra. In addition, we introduce an ansatz that exploits the locality of the contributions to the g-tensor for molecules with local spin density. For such systems, sublinear scaling is obtained by restricting the magnetic field perturbation to the relevant subspaces of the full atomic orbital space; several criteria for selecting these subspaces are discussed and compared. It is shown that the computational cost of g-tensor calculations with the local approach can fall below the cost of the self-consistent field calculation for large molecules. The presented methods thus enable efficient, accurate, and gauge-origin independent computations of electronic g-tensors of large molecular systems.
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Nat Commun
January 2025
Quantum Research Center, Technology Innovation Institute, Abu Dhabi, UAE.
Quantum computers hold the promise of more efficient combinatorial optimization solvers, which could be game-changing for a broad range of applications. However, a bottleneck for materializing such advantages is that, in order to challenge classical algorithms in practice, mainstream approaches require a number of qubits prohibitively large for near-term hardware. Here we introduce a variational solver for MaxCut problems over binary variables using only n qubits, with tunable k > 1.
View Article and Find Full Text PDFElife
December 2024
Howard Hughes Medical Institute, Stanford University, Stanford, United States.
Defining the cellular factors that drive growth rate and proteome composition is essential for understanding and manipulating cellular systems. In bacteria, ribosome concentration is known to be a constraining factor of cell growth rate, while gene concentration is usually assumed not to be limiting. Here, using single-molecule tracking, quantitative single-cell microscopy, and modeling, we show that genome dilution in cells arrested for DNA replication limits total RNA polymerase activity within physiological cell sizes across tested nutrient conditions.
View Article and Find Full Text PDFNat Commun
November 2024
Instituto Gulbenkian de Ciência (IGC), Oeiras, Portugal.
Both metabolism and growth scale sublinearly with body mass across species. Ecosystems show the same sublinear scaling between production and total biomass, but ecological theory cannot reconcile the existence of these nearly identical scalings at different levels of biological organization. We attempt to solve this paradox using marine phytoplankton, connecting individual and ecosystem scalings across three orders of magnitude in body size and biomass.
View Article and Find Full Text PDFProc Biol Sci
October 2024
Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA.
Organisms can learn in response to environmental inputs as well as actively modify their environments through niche construction on slower evolutionary time scales. How quickly should an organism respond to a changing environment, and when possible, should organisms adjust the time scale of environmental change? We formulate these questions using a model of learning costs that considers optimal time scales of both memory and environment. We derive a general, sublinear scaling law for optimal memory as a function of environmental persistence.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
October 2024
Laboratory of Catchment Hydrology and Geomorphology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Sion 1951, Switzerland.
Allometric scaling relations are widely used to link biological processes to body size in nature. Several studies have shown that such scaling laws hold also for natural ecosystems, including individual trees and forests, riverine metabolism, and river network organization. However, the derivation of scaling laws for catchment-scale water and carbon fluxes has not been achieved so far.
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