A robust, simple, and efficient convergence workflow for GW calculations

The formic acid molecule, its dimers, and its molecular crystal are adopted as test systems to compare results obtained with plane wave (PW) basis sets and norm-conserving pseudopotentials to all-electron Gaussian-type orbital (GTO) calculations. The CPMD and CRYSTAL06 codes, respectively, are applied with the PBE, PW91, and BLYP density functionals. Hydrogen bonding is the leading interaction in the dimers and the crystal. In the latter, dispersive and weak C-H...O interactions are also relevant. Irrespective of the adopted functional, for all considered structures PW and GTO results converge smoothly as a function of the quality of the adopted basis sets to the same values for structures, energies of interaction, and harmonic vibrational features. To achieve a high level of mutual agreement the use of GTO basis sets of at least of triple-zeta quality including one set of polarization functions and PW basis sets with a kinetic energy cutoff higher than 110 Ry is recommended. Pros and cons of both approaches for studying molecular crystals are also discussed.

The reliability of the random phase approximation (RPA) and of σ-functional methods in conjunction with the mixed Gaussian and plane wave (GPW) basis set scheme as implemented in the CP2K package is investigated. First, based on the results for thermochemical properties of molecules and structural properties of crystalline solids, we establish reliable computational setups for practical calculations. Next, we compare the results obtained with these setups to those from standard GPW basis set approaches. For molecules, the results of RPA and σ-functional calculations within the GPW scheme are slightly worse, though still comparable, to those obtained using the standard Gaussian basis set scheme, provided a large enough orbital basis set is employed in the GPW calculations. Furthermore, the GPW calculations using σ-functionals are clearly more accurate than RPA calculations and even more so than those of conventional Kohn-Sham methods in the prediction of reaction energies and barrier heights for main group chemistry. For crystalline solids, the RPA and σ-functional methods significantly outperform the conventional Perdew-Burke-Ernzerhof (PBE) method in determining lattice constants. However, only the RPA method provides improved results for bulk moduli, while the σ-functional method yields errors comparable to those of the PBE method. A comparison of the results of the plane wave basis set calculations with the projector augmented wave method shows reasonable consistency for lattice constants and bulk moduli.

Finite temperature ab initio molecular dynamics (AIMD), in which forces are obtained from "on-the-fly" electronic structure calculations, is a widely used technique for studying structural and dynamical properties of chemically active systems. Recently, we introduced an AIMD scheme based on discrete variable representation (DVR) basis sets, which was shown to have improved convergence properties over the conventional plane wave (PW) basis set [Liu,Y.; et al. Phys. Rev. B 2003, 68, 125110]. In the present work, the numerical algorithms for the DVR based AIMD scheme (DVR/AIMD) are provided in detail, and the latest developments of the approach are presented. The accuracy and stability of the current implementation of the DVR/AIMD scheme are tested by performing a simulation of liquid water at ambient conditions. The structural information obtained from the present work is in good agreement with the result of recent AIMD simulations with a PW basis set (PW/AIMD). Advantages of using the DVR/AIMD scheme over the PW/AIMD method are discussed. In particular, it is shown that a DVR/AIMD simulation of liquid water in the complete basis set limit is possible with a relatively small number of grid points.

A quantitative proteomic workflow for characterization of frozen clinical biopsies: laser capture microdissection coupled with label-free mass spectrometry.

Background: Over the last decade, we have observed in microbial ecology a transition from gene-centric to genome-centric analyses. Indeed, the advent of metagenomics combined with binning methods, single-cell genome sequencing as well as high-throughput cultivation methods have contributed to the continuing and exponential increase of available prokaryotic genomes, which in turn has favored the exploration of microbial metabolisms. In the case of metagenomics, data processing, from raw reads to genome reconstruction, involves various steps and software which can represent a major technical obstacle. Methods: To overcome this challenge, we developed SnakeMAGs, a simple workflow that can process Illumina data, from raw reads to metagenome-assembled genomes (MAGs) classification and relative abundance estimate. It integrates state-of-the-art bioinformatic tools to sequentially perform: quality control of the reads (illumina-utils, Trimmomatic), host sequence removal (optional step, using Bowtie2), assembly (MEGAHIT), binning (MetaBAT2), quality filtering of the bins (CheckM), classification of the MAGs (GTDB-Tk) and estimate of their relative abundance (CoverM). Developed with the popular Snakemake workflow management system, it can be deployed on various architectures, from single to multicore and from workstation to computer clusters and grids. It is also flexible since users can easily change parameters and/or add new rules. Results: Using termite gut metagenomic datasets, we showed that SnakeMAGs is slower but allowed the recovery of more MAGs encompassing more diverse phyla compared to another similar workflow named ATLAS. Conclusions: Overall, it should make the reconstruction of MAGs more accessible to microbiologists. SnakeMAGs as well as test files and an extended tutorial are available at https://github.com/Nachida08/SnakeMAGs.

Background: Over the last decade, we have observed in microbial ecology a transition from gene-centric to genome-centric analyses. Indeed, the advent of metagenomics combined with binning methods, single-cell genome sequencing as well as high-throughput cultivation methods have contributed to the continuing and exponential increase of available prokaryotic genomes, which in turn has favored the exploration of microbial metabolisms. In the case of metagenomics, data processing, from raw reads to genome reconstruction, involves various steps and software which can represent a major technical obstacle.Methods: To overcome this challenge, we developed SnakeMAGs, a simple workflow that can process Illumina data, from raw reads to metagenome-assembled genomes (MAGs) classification and relative abundance estimate. It integratesstate-of-the-art bioinformatic tools to sequentially perform: quality control of the reads (illumina-utils, Trimmomatic), host sequence removal (optional step, using Bowtie2), assembly (MEGAHIT), binning (MetaBAT2), quality filtering of the bins (CheckM, GUNC), classification of the MAGs (GTDB-Tk) and estimate of their relative abundance (CoverM). Developed with the popular Snakemake workflow management system, it can be deployed on various architectures, from single to multicore and from workstation to computer clusters and grids. It is also flexible since users can easily change parameters and/or add new rules.Results: Using termite gut metagenomic datasets, we showed thatSnakeMAGs is slower but allowed the recovery of more MAGs encompassing more diverse phyla compared to another similar workflow named ATLAS. Importantly, these additional MAGs showed no significant difference compared to the other ones in terms of completeness, contamination, genome size nor relative abundance.Conclusions: Overall, it should make the reconstruction of MAGs more accessible to microbiologists.SnakeMAGs as well as test files and an extended tutorial are available athttps://github.com/Nachida08/SnakeMAGs.

Density functional theory calculations with plane-wave basis sets are often used for theoretical investigations of solid materials; nevertheless, analysis techniques for open shell structures are insufficient. In this study, we established an electron density-based estimation scheme for the diradical character (y). The values estimated by the proposed and conventional schemes are consistent. Additionally, the estimated y values are qualitatively the same as the experimentally obtained values, and the values obtained using plane-wave and atom-centred basis sets are equal.

This study demonstrates linear scaling with system size in fixednode diffusion QMC calculations with pseudopotentials for carbon fullerenes and for hydrogenated silicon clusters with nearly 1000 valence electrons. The range covered is C20 to C18o and SiH4 to Si211H140, The calculations were carried out with standard pseudopotentials (leaving four electrons per C or Si atom) and Slater Jastrow trial functions with orbitals from LDA density functional calculations. The key elements leading to linear scaling were constructions of sparse determinants and simplification of orbital functions. The LDA orbitals were obtained with a plane-wave basis set which was transformed to yield Wannier functions with maximum localization. These functions were then fitted to cubic spline expressions with a limited number of grid points and cut-off distances matching those of the plane-wave orbitals. The computational effort for evaluating trial functions of this type was clearly seen to behave in a linear fashion. Exploration of the effects of variation of cut-off radius revealed that energies, sensitive only to node structures, increased rapidly for cut-offs less than 7 bohr but were unaffected for cutoffs greater than 7 bohr. The accuracies of the calculations were checked in other ways. These included comparison calculations for SiH3 and Si5H12 using Gaussian, original plane-wave, and Wannier function basis sets which gave close agreement for energies.

The second-order Møller-Plesset perturbation (MP2) theory is a post-Hartree-Fock method widely used to describe weak correlation energies in solids and molecules, but its high computational cost scales as O(N5). Herein, we present an accurate and efficient implementation of MP2 within the plane-wave (PW) basis set for both periodic and molecular systems, which incorporates the interpolative separable density fitting (ISDF) decomposition and the Laplace transformation (LT) of the energy denominator. These innovations avoid the direct construction of electron repulsion integrals (ERIs) and reduce the computational complexity of MP2 from O(N5) to O(N4). The key idea for reducing the scaling is to exploit the numerical redundancy of occupied-virtual molecular orbital pairs on the real-space grid in the plane-wave basis set, which enables ERIs to be factorized into lower-rank quantities. This leads to further cost reductions in both the direct and exchange terms of the MP2 correlation energy. For a bulk silicon system consisting of 128 atoms, the LT-ISDF-MP2 method demonstrates a 13.5-fold speedup in total computation time compared to the standard approach. Using this plane-wave LT-ISDF-MP2 method, we simulate the π-π stacking interaction in the 1,3-butadiene dimer, successfully capturing the dispersion interaction and reproducing the self-assembled configuration.

Full configuration interaction (FCI) calculations have historically faced significant challenges in dealing with periodic systems. The plane-wave basis sets are valued for their efficiency and broad applicability in various computational physics and chemistry simulations. Because of their natural periodicity, the plane-wave basis sets offer a potential solution to this problem. Moreover, FCI can address the limitations of widely used methods, such as density functional theory (DFT) with plane-wave basis sets, in accurately describing strongly correlated systems. However, the large basis set nature of the plane-wave makes them unsuitable for direct application in FCI calculations. To address this challenge, we propose an improved algorithm based on the correlation-optimized virtual orbital (COVOS) framework. By incorporating rotational matrices to enhance the active space dimension and optimizing orbitals through iterative coupled processes, we successfully compress the extensive plane-wave basis set into a manageable number of virtual orbitals suitable for FCI calculations while retaining most of the original basis set characteristics. We apply this method to supercell calculations and potential energy curves of periodic metallic systems. To further validate our approach, we test it on nonperiodic small molecular systems and compare the results with those obtained from DFT, second-order Møller-Plesset perturbation theory (MP2), random phase approximation (RPA), FCI calculations using the 6-31G or cc-pVDZ basis sets, and the original COVOS algorithm. The improved COVOS framework demonstrates significant advantages in convergence and correlation description over the original method. Furthermore, we observe metal divergence issues in MP2 calculations for certain metallic systems and note that RPA may overestimate the correlation energy of such systems. These findings underscore the importance of achieving FCI calculations with plane-wave basis sets.

The multi-attribute method (MAM) by liquid chromatography-mass spectrometry peptide mapping has the potential to replace multiple conventional HPLC- and capillary electrophoresis-based purity/impurity assays for release and stability testing of protein biopharmaceuticals such as monoclonal antibodies. Prerequisite is the availability of the new peak detection (NPD) functionality to reliably detect new, absent, and changed peptide species that may impair the quality, safety, and efficacy of the drug. Here, we describe the development, qualification, and application of a highly efficient and robust NPD workflow within the Genedata Expressionist® software. The detection thresholds have been rationally designed, and the NPD workflow has been successfully validated according to ICH Q2 guidelines. Individual case studies, including stability testing of drug product and detection of unknown impurities in drug substance, highlight the workflows’ ability to reliably recognize relevant peptide species below 1% relative abundance without reporting any false positive peaks. The application of this NPD workflow signifies a substantial leap forward in the use of MAM as a quality control tool, as it allows identification of true positive peaks at adequate sensitivity in the absence of false positive peaks.

PWDFT.jl: A Julia package for electronic structure calculation using density functional theory and plane wave basis

We introduced two methods to correct the singularity in the calculation of long-range Hartree-Fock (HF) exchange for long-range-corrected density functional theory (LC-DFT) calculations in plane-wave basis sets. The first method introduces an auxiliary function to cancel out the singularity. The second method introduces a truncated long-range Coulomb potential, which has no singularity. We assessed the introduced methods using the LC-BLYP functional by applying it to isolated systems of naphthalene and pyridine. We first compared the total energies and the HOMO energies of the singularity-corrected and uncorrected calculations and confirmed that singularity correction is essential for LC-DFT calculations using plane-wave basis sets. The LC-DFT calculation results converged rapidly with respect to the cell size as the other functionals, and their results were in good agreement with the calculated results obtained using Gaussian basis sets. LC-DFT succeeded in obtaining accurate orbital energies and excitation energies. We next applied LC-DFT with singularity correction methods to the electronic structure calculations of the extended systems, Si and SiC. We confirmed that singularity correction is important for calculations of extended systems as well. The calculation results of the valence and conduction bands by LC-BLYP showed good convergence with respect to the number of k points sampled. The introduced methods succeeded in overcoming the singularity problem in HF exchange calculation. We investigated the effect of the singularity correction on the excitation state calculation and found that careful treatment of the singularities is required compared to ground-state calculations. We finally examined the excitonic effect on the band gap of the extended systems. We calculated the excitation energies to the first excited state of the extended systems using a supercell model at the Γ point and found that the excitonic binding energy, supposed to be small for inorganic semiconductors, was quite large. Our findings suggest that more investigation on the effect of the excitonic binding energy on band gaps is necessary.

Self-consistent solutions of the Dyson equation are obtained using a plane-wave basis set for seven small molecules. Such self-consistent solutions can help to unify the different $GW$ self-consistent schemes, reduce the scatter of results in current $GW$ calculations, and shed light on the true effects of $GW$ self-consistency. Unlike other works of self-consistent $GW$ calculations, in the present work the Green's function is expressed as a matrix under the plane-wave basis set. The algorithmic details which enable such calculations are presented. The ability to solve the full Green's function using a plane-wave basis set may open the door for future beyond-$GW$ many-body perturbation theory calculations.

Data-driven machine learning force fields (MLFs) are more and more popular in atomistic simulations and exploit machine learning methods to predict energies and forces for unknown structures based on the knowledge learned from an existing reference database. The latter usually comes from density functional theory calculations. One main drawback of MLFs is that physical laws are not incorporated in the machine learning models, and instead, MLFs are designed to be very flexible to simulate complex quantum chemistry potential energy surface (PES). In general, MLFs have poor transferability, and hence, a very large trainset is required to span all the target feature space to get a reliable MLF. This procedure becomes more troublesome when the PES is complicated, with a large number of degrees of freedom, in which building a large database is inevitable and very expensive, especially when accurate but costly exchange-correlation functionals have to be used. In this manuscript, we exploit a high-dimensional neural network potential (HDNNP) on Pt clusters of sizes from 6 to 20 as one example. Our standard level of energy calculation is DFT GGA (PBE) using a plane wave basis set. We introduce an approximate but fast level with the PBE functional and a minimal atomic orbital basis set, and then, a more accurate but expensive level, using a hybrid functional or nonlocal vdW functional and a plane wave basis set, is reliably predicted by learning the difference with HDNNP. The results show that such a differential approach (named ΔHDNNP) can deliver very accurate predictions (error <10 meV/atom) in reference to converged basis set energies as well as more accurate but expensive xc functionals. The overall speedup can be as large as 900 for a 20 atom Pt cluster. More importantly, ΔHDNNP shows much better transferability due to the intrinsic smoothness of the delta potential energy surface, and accordingly, one can use much smaller trainset data to obtain better accuracy than the conventional HDNNP. A multilayer ΔHDNNP is thus proposed to obtain very accurate predictions versus expensive nonlocal vdW functional calculations in which the required trainset is further reduced. The approach can be easily generalized to any other machine learning methods and opens a path to study the structure and dynamics of Pt clusters and nanoparticles.