Ab initio density response and local field factor of warm dense hydrogen

We present quasi-exact ab initio path integral Monte Carlo(PIMC)results for the partial static density responses and local field factors of hydrogen in the warm dense matter regime,from solid density conditions to the strongly compressed case.The full dynamic treatment of electrons and protons on the same footing allows us to rigorously quantify both electronic and ionic exchange–correlation effects in the system,and to compare the results with those of earlier incomplete models such as the archetypal uniform electron gas or electrons in a fixed ion snapshot potential that do not take into account the interplay between the two constituents.The full electronic density response is highly sensitive to electronic localization around the ions,and our results constitute unambiguous predictions for upcoming X-ray Thomson scattering experiments with hydrogen jets and fusion plasmas.All PIMC results are made freely available and can be used directly for a gamut of applications,including inertial confinement fusion calculations and the modeling of dense astrophysical objects.Moreover,they constitute invaluable benchmark data for approximate but computationally less demanding approaches such as density functional theory or PIMC within the fixed-node approximation.

Physical insights from imaginary-time density–density correlation functions

An accurate theoretical description of the dynamic properties of correlated quantum many-body systems,such as the dynamic structure factor S(q,ω),is important in many fields.Unfortunately,highly accurate quantum Monte Carlo methods are usually restricted to the imaginary time domain,and the analytic continuation of the imaginary-time density–density correlation function F(q,τ)to real frequencies is a notoriously hard problem.Here,it is argued that often no such analytic continuation is required because by definition,F(q,τ)contains the same physical information as does S(q,ω),only represented unfamiliarly.Specifically,it is shown how one can directly extract key information such as the temperature or quasi-particle excitation energies from theτdomain,which is highly relevant for equation-of-state measurements of matter under extreme conditions[T.Dornheim et al.,Nat.Commun.13,7911(2022)].As a practical example,ab initio path-integral Monte Carlo results for the uniform electron gas(UEG)are considered,and it is shown that even nontrivial processes such as the roton feature of the UEG at low density[T.Dornheim et al.,Commun.Phys.5,304(2022)]are manifested straightforwardly in F(q,τ).A comprehensive overview is given of various useful properties of F(q,τ)and how it relates to the usual dynamic structure factor.In fact,working directly in theτdomain is advantageous for many reasons and opens up multiple avenues for future applications.

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19

The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supplyconstrained resource allocation strategies for controlling novel disease epidemics.To address the challenge of constrained resource optimization for managing diseases with complications like pre-and asymptomatic transmission,we develop an integro partial differential equation compartmental disease model which incorporates realistic latent,incubation,and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals.Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission.To analyze the influence of these realistic features on disease controllability,we find optimal strategies for reducing total infection sizes that allocate limited testing resources between‘clinical’testing,which targets symptomatic individuals,and‘non-clinical’testing,which targets non-symptomatic individuals.We apply our model not only to the original,delta,and omicron COVID-19 variants,but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions,which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness.We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies,while the relationship between incubation-latent mismatch,controllability,and optimal strategies is complicated.In particular,though greater degrees of presymptomatic transmission reduce disease controllability,they may increase or decrease the role of nonclinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length.Importantly,our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.

Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics

A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials(ML-IAPs)for largescale spin-lattice dynamics simulations.The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin model with an ML-IAP.Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed.Both the atomic spin model and the ML-IAP are parametrized on data from first-principles calculations.We demonstrate the efficacy of our data-driven framework across magneto-structural phase transitions by generating a magneto-elastic ML-IAP forα-iron.The combined potential energy surface yields excellent agreement with firstprinciples magneto-elastic calculations and quantitative predictions of diverse materials properties including bulk modulus,magnetization,and specific heat across the ferromagnetic–paramagnetic phase transition.

Current status of greenhouse gas emissions from aquaculture in China

Aquaculture and mariculture are becoming an increasingly important source of food supply in many countries and regions.However,with the expansion of aquaculture and mariculture comes increasing emissions of greenhouse gases(GHG)which contribute to global warming and climate change.China leads the world in aquaculture and mariculture production,but there are no studies that systematically assess China's overall carbon footprint from these industries.This study quantified GHG emissions from aquaculture and mariculture by four source phases(feed,energy use,nitrous oxide and fertilizers),and then analyzed the carbon footprint of each of these phases for GHG production of nine major species groups over the past ten years to show the spatial distribution of GHG emissions from aquaculture and mariculture in China.Our results showed that the production of feed materials contributed most to the GHG emissions and found that crop energy use,crop land use changes(LUC),fertilizer production,crop nitrous oxide production and rice methane production were the main sources of feed emissions.The total GHG emissions of the nine species groups were 112 Mt(10^(9) kg)CO_(2)e,the nine species accounting for approximately 86%of aquaculture and mariculture production.GHG emissions of cyprinids had the highest contribution at 47%.Spatial analysis based on our study showed Guangdong,Hubei,Jiangsu and Shandong had the highest GHG emissions of all the provinces in this study,and they accounted for approximately 46%of all emissions.The regional Gross Domestic Product(GDP)was significantly positively correlated with GHG emissions in every province,with a correlation coefficient higher than 0.6.Our results showed for the first time the relationship between the relative production by species composition and spatial distribution of GHG emissions from aquaculture and mariculture in China.Our findings provide the scientific basis for reduction of GHG emissions within a broader context of expanding aquaculture in the future.