PyPWDFT: A Lightweight Python Software for Single-Node 10K Atom Plane-Wave Density Functional Theory Calculations.

PyPWDFT is a Python software designed for performing plane-wave density functional theory (DFT) calculations. It can perform large-scale DFT calculations using only a single process on a single node, including local density functional for 10,000 atoms and nonlocal hybrid functional for 4096 atoms. Our benchmark test results demonstrate that PyPWDFT achieves performance comparable to that of Fortran/C++ codes, despite being developed in a native Python environment.

Projected Wave Function Extrapolation Scheme to Accelerate Plane-Wave Hybrid Functional-Based Born-Oppenheimer Molecular Dynamics Simulations.

Born-Oppenheimer molecular dynamics (BOMD) simulations are of great interest for the dynamic properties of molecular and solid systems. However, BOMD simulations necessitate not only an extensive period of dynamical evolution but also costly self-consistent-field (SCF) electronic structure calculations, especially for hybrid functional-based BOMD (H-BOMD) simulations within plane-wave basis sets. Here, we propose an improved always stable predictor-corrector (ASPC) method for the wave function extrapolation to accelerate the plane-wave H-BOMD simulations, named projected ASPC (PASPC), yielding a wave function closer to the actual solution space and efficiently reducing the number of SCF iterations at each MD step.

A generative model for inorganic materials design.

The design of functional materials with desired properties is essential in driving technological advances in areas such as energy storage, catalysis and carbon capture. Generative models accelerate materials design by directly generating new materials given desired property constraints, but current methods have a low success rate in proposing stable crystals or can satisfy only a limited set of property constraints. Here we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints.

Landform and lithospheric development contribute to the assembly of mountain floras in China.

Although it is well documented that mountains tend to exhibit high biodiversity, how geological processes affect the assemblage of montane floras is a matter of ongoing research. Here, we explore landform-specific differences among montane floras based on a dataset comprising 17,576 angiosperm species representing 140 Chinese mountain floras, which we define as the collection of all angiosperm species growing on a specific mountain. Our results show that igneous bedrock (granitic and karst-granitic landforms) is correlated with higher species richness and phylogenetic overdispersion, while the opposite is true for sedimentary bedrock (karst, Danxia, and desert landforms), which is correlated with phylogenetic clustering.

Machine Learning K-Means Clustering in Interpolative Separable Density Fitting Algorithm: Advancing Accurate and Efficient Cubic-Scaling Density Functional Perturbation Theory Calculations within Plane Waves.

Density functional perturbation theory (DFPT) is a crucial tool for accurately describing lattice dynamics. The adaptively compressed polarizability (ACP) method reduces the computational complexity of DFPT calculations from O() to O() by combining the interpolative separable density fitting (ISDF) algorithm. However, the conventional QR factorization with column pivoting (QRCP) algorithm, used for selecting the interpolation points in ISDF, not only incurs a high cubic-scaling computational cost, O(), but also leads to suboptimal convergence.

Complex-Valued K-Means Clustering of Interpolative Separable Density Fitting Algorithm for Large-Scale Hybrid Functional Enabled Molecular Dynamics Simulations within Plane Waves.

K-means clustering, as a classic unsupervised machine learning algorithm, is the key step to select the interpolation sampling points in interpolative separable density fitting (ISDF) decomposition for hybrid functional electronic structure calculations. Real-valued K-means clustering for accelerating the ISDF decomposition has been demonstrated for large-scale hybrid functional enabled molecular dynamics (hybrid AIMD) simulations within plane-wave basis sets where the Kohn-Sham orbitals are real-valued. However, it is unclear whether such K-means clustering works for complex-valued Kohn-Sham orbitals.

High-Throughput Screening of Rattling-Induced Ultralow Lattice Thermal Conductivity in Semiconductors.

Thermoelectric (TE) materials with rattling model show ultralow lattice thermal conductivity for high-efficient energy conversion between heat and electricity. In this work, by analysis of the key spirit of the rattling model, we propose an efficient empirical descriptor to realize the high-throughput screening of ultralow thermal conductivity in a series of semiconductors. This descriptor extracts the structural information of rattling atoms whose bond lengths with all the nearest neighboring atoms are larger than the sum of corresponding covalent radiuses.

Realizing Effective Cubic-Scaling Coulomb Hole Plus Screened Exchange Approximation in Periodic Systems via Interpolative Separable Density Fitting with a Plane-Wave Basis Set.

The GW approximation is an effective way to accurately describe the single-electron excitations of molecules and the quasiparticle energies of solids. However, a perceived drawback of the GW calculations is their high computational cost and large memory usage, which limit their applications to large systems. Herein, we demonstrate an accurate and effective low-rank approximation to accelerate non-self-consistent GW (GW) calculations under the static Coulomb hole plus screened exchange (COHSEX) approximation for periodic systems.

Study of Brain Structure in HIV Vertically Infected Adolescents.

Neuroimaging studies have focused mainly on human immunodeficiency virus (HIV)-infected adults or younger children, showing abnormal brain structures. In this study, we used voxel-based morphometry to investigate the brain integrity of HIV vertically infected adolescents. Twenty-five HIV vertically infected (HIV+) adolescents and 33 HIV-exposed, but uninfected (HIV-) and demographically matched controls participated in this study.

Machine Learning K-Means Clustering Algorithm for Interpolative Separable Density Fitting to Accelerate Hybrid Functional Calculations with Numerical Atomic Orbitals.

The interpolative separable density fitting (ISDF) is an efficient and accurate low-rank decomposition method to reduce the high computational cost and memory usage of the Hartree-Fock exchange (HFX) calculations with numerical atomic orbitals (NAOs). In this work, we present a machine learning K-means clustering algorithm to select the interpolation points in ISDF, which offers a much cheaper alternative to the expensive QR factorization with column pivoting (QRCP) procedure. We implement this K-means-based ISDF decomposition to accelerate hybrid functional calculations with NAOs in the HONPAS package.

Asymptomatic Human Immunodeficiency Virus Vertical Transmitted Adolescents' Brain Functional Changes: Based on Resting-State Functional Magnetic Resonance Imaging.

Perinatal HIV-infected (PHIV+) adolescents survive longer with the use of readily found combination antiretroviral therapy (cART); however, they still have the risk of developing cognitive deficits. The article aims to explore the brain functional changes in asymptomatic PHIV+ adolescents with cART based on the resting-state functional magnetic resonance imaging (rs-fMRI). rs-fMRI was performed on 20 PHIV+ adolescents and 28 PHIV- controls to evaluate the regional homogeneity (ReHo) in different brain regions by calculating the Kendall harmonious coefficient.

Structural Covariance of Gray Matter Volume in HIV Vertically Infected Adolescents.

Human immunodeficiency virus (HIV) infection significantly affect neurodevelopmental and behavioral outcomes. We investigated whether alterations of gray matter organization and structural covariance networks with vertical HIV infection adolescents exist, by using the GAT toolbox. MRI data were analysed from 25 HIV vertically infected adolescents and 33 HIV-exposed-uninfected control participants.

Altered Activation in Cerebellum Contralateral to Unilateral Thalamotomy May Mediate Tremor Suppression in Parkinson's Disease: A Short-Term Regional Homogeneity fMRI Study.

Background: Ventral intermediate nucleus thalamotomy is an effective treatment for Parkinson's disease tremor. However, its mechanism is still unclear.

Purpose: We used resting-state fMRI to investigate short-term ReHo changes after unilateral thalamotomy in tremor-dominant PD, and to speculate about its possible mechanism on tremor suppression.

Altered spontaneous brain activity in patients with acute spinal cord injury revealed by resting-state functional MRI.

Background: Previous neuroimaging studies have provided evidence of structural and functional reorganization of brain in patients with chronic spinal cord injury (SCI). However, it remains unknown whether the spontaneous brain activity changes in acute SCI. In this study, we investigated intrinsic brain activity in acute SCI patients using a regional homogeneity (ReHo) analysis based on resting-state functional magnetic resonance imaging.

Identification of cisplatin-resistance associated genes through proteomic analysis of human ovarian cancer cells and a cisplatin-resistant subline.

Chemoresistance to cancer therapy is a major obstacle to the effective treatment of human cancers with cisplatin (DDP), but the mechanisms of cisplatin-resistance are not clear. In this study, we established a cisplatin- resistant human ovarian cancer cell line (COC1/DDP) and identified differentially expressed proteins related to cisplatin resistance. The proteomic expression profiles in COC1 before and after DDP treatment were examined using 2-dimensional electrophoresis technology.