Force and stress calculations with a neural-network wave function for solids.

Accurate calculations of real solids are of fundamental importance in fields such as chemistry, phases and materials science. Recently, variational Monte Carlo (VMC) based on neural-network wave functions has been developed as a promising option to solve the existing challenges in calculations. In this study, we discuss the calculation of interatomic forces and stress tensors of real solids with a neural-network-based VMC method.

Electric Polarization from a Many-Body Neural Network Ansatz.

Ab initio calculation of dielectric response with high-accuracy electronic structure methods is a long-standing problem, for which mean-field approaches are widely used and electron correlations are mostly treated via approximated functionals. Here we employ a neural network wave function ansatz combined with quantum Monte Carlo method to incorporate correlations into polarization calculations. On a variety of systems, including isolated atoms, one-dimensional chains, two-dimensional slabs, and three-dimensional cubes, the calculated results outperform conventional density functional theory and are consistent with the most accurate calculations and experimental data.

Interatomic force from neural network based variational quantum Monte Carlo.

Accurate ab initio calculations are of fundamental importance in physics, chemistry, biology, and materials science, which have witnessed rapid development in the last couple of years with the help of machine learning computational techniques such as neural networks. Most of the recent efforts applying neural networks to ab initio calculation have been focusing on the energy of the system. In this study, we take a step forward and look at the interatomic force obtained with neural network wavefunction methods by implementing and testing several commonly used force estimators in variational quantum Monte Carlo (VMC).

Study on Willingness to Pay and Impact Mechanism of Gutter Oil Treatment: Taking Urban Residents in Sichuan Province as an Example.

"Gutter oil" is a term for the practice of recycling used waste oil from restaurant fryers, sinks, and even slaughterhouses and sewers, and has been a major food safety and sanitation issue in China for many years. However, with proper treatment, these issues can be mitigated, turning large amounts of waste product into valuable resources and conserving energy resources. Based on this questionnaire survey conducted in the cities of Chengdu and Mianyang in Sichuan, China, this paper uses the value evaluation method to measure urban residents' willingness to pay for the treatment of gutter oil, and explores the factors and path influencing residents' willingness to pay based on the extended theory of planned behavior.

Dynamic analysis of agricultural carbon emissions efficiency in Chinese provinces along the Belt and Road.

To better understand the agricultural resources and environmental problems of the provinces along The Belt and Road in China, it is critical to investigate their agricultural carbon emission efficiency and evolutionary trends. Based on the panel data of 18 key provinces and cities between 2006 and 2015, this paper evaluated the agricultural carbon emission efficiency with the data envelopment analysis-Malmquist model and further explored their dynamic evolutionary trends. There were several main findings.