Chemical Software Research Articles

Design, microwave synthesis, characterization and antimicrobial activity of imidazolone derivatives

Overuse and misuse of antibiotics create superbugs as drug-resistant bacteria. The vital strategy is developing new antibiotics, and one of the vital compounds that proved activity as antimicrobials is imidazoles. So, we designed and synthesized a series of imidazolones via microwave radiation in excellent yield and evaluated them as antimicrobial agents with docking studies to promote and boost their activity. The results obtained from spectroscopic analyses approved the chemical structures of new compounds. The biological antimicrobial activity results was found that these compounds exert an excellent broad spectrum antimicrobial activity against many Gram-positive and Gram-negative bacteria, as well as different fungal strain including some highly human pathogenic microorganisms.Compounds 5a-5c are active against Gram-positive bacteria and compounds 5d-5f against Gram-negative bacteria, together with Candida albicans as fungi. Molecular Docking study on the active compounds 5e, 5f showed ring-stacking interactions between them as ligands and related proteins, which are compatible with other results. Gaussian 09 program is used as computational chemistry software to confirm the obtained experimental results.

Scientific and Regulatory Lessons Learnt on Building a Chemistry, Manufacturing, and Controls (CMC) Package for COVID-19 Variant Vaccine Updates in the EU—A Regulator’s Perspective

During the COVID-19 pandemic, eight COVID-19 vaccines were authorised in the European Union (EU); as a result of emerging SARS-CoV-2 variants and waning immunity, some of these have been adapted to broaden the immunity against circulating variants. The pace at which variants emerge challenges the technical feasibility to make adapted vaccines available in a suitable timeframe and in sufficient quantities. Despite the current absence of a clear-cut seasonal spread for COVID-19, the EU regulatory approach thus far is a pragmatic approach following a pathway similar to that of seasonal influenza. This approach currently requires chemistry, manufacturing, and controls (CMC—the design, development and consistent manufacture of a specified medicinal product of good quality) and non-clinical data (from product laboratory and animal studies), as well as demonstrating that updated vaccines induce an immune response that can predict clinical efficacy and safety in humans. For CMC data, COVID-19 mRNA vaccine adaptations generally made use of the same formulation, control strategy, manufacturing process, and inclusion of registered manufacturing sites for the drug product; therefore assessment was generally streamlined. The experience gained from the vaccine adaptations, combined with a continuous early regulator-developer scientific discussion, permits increasingly greater predictability for timing and positive regulatory outcomes. Here, we review key aspects of the quality control and manufacture of updating COVID-19 vaccines to protect against new variants. Although most experience has been gained with mRNA vaccines, we note that investment in the streamlining of manufacturing processes for recombinant protein vaccines would facilitate future strain updates/adaptations thereby safeguarding availability of different COVID-19 vaccine types, which is considered of value for public health. We also reflect on the challenges and opportunities in establishing more predictable regulatory mechanisms for future COVID-19 vaccine adaptions and more widely for future vaccines containing rapidly evolving pathogens with the potential to cause health threats.

Electronic structure simulations in the cloud computing environment.

The transformative impact of modern computational paradigms and technologies, such as high-performance computing (HPC), quantum computing, and cloud computing, has opened up profound new opportunities for scientific simulations. Scalable computational chemistry is one beneficiary of this technological progress. The main focus of this paper is on the performance of various quantum chemical formulations, ranging from low-order methods to high-accuracy approaches, implemented in different computational chemistry packages and libraries, such as NWChem, NWChemEx, Scalable Predictive Methods for Excitations and Correlated Phenomena, ExaChem, and Fermi-Löwdin orbital self-interaction correction on Azure Quantum Elements, Microsoft's cloud services platform for scientific discovery. We pay particular attention to the intricate workflows for performing complex chemistry simulations, associated data curation, and mechanisms for accuracy assessment, which is demonstrated with the Arrows automated workflow for high throughput simulations. Finally, we provide a perspective on the role of cloud computing in supporting the mission of leadership computational facilities.

Potassium sulphate production from an aqueous sodium sulphate from lead‐acid battery recycling: Impact of feedstock impurities on products yields

AbstractThe increasing demand for renewable energy highlights the need for efficient energy storage solutions. Despite various available technologies, lead‐acid batteries remain preferred for many industrial applications due to their inherent advantages. However, their expanded use necessitates proper waste management and recycling practices. During lead‐acid battery recycling, Na₂SO₄ is generated as a waste product, which cannot be directly sold due to quality concerns and limited market demand. Consequently, advanced waste management techniques are required to comply with government regulations on industrial waste disposal. Despite these challenges, Na2SO4 serves as a vital precursor for producing K2SO4, a valuable fertilizer. Prior research on the glaserite process for converting Na2SO4 to K2SO4 has assumed Na2SO4 to be pure—without traces of impurities. However, Na2SO4 recovered from battery recycling contains various contaminants. To address this, HSC Chemistry software was used to model K2SO4 and NaCl production from impure Na2SO4 and KCl, considering feed impurities. Under ideal conditions—a 1 bar pressure, 25°C feed temperature, and 40°C reactor temperature—over 90% yield of K2SO4 and NaCl was achieved in the absence of impurities. However, the addition of impurities resulted in a reduction in yields. Notably, impurity levels ranging from 1% to 4% by weight still allowed for yields exceeding 90%. Furthermore, a review of reactor compositions revealed a significant depletion of potassium and chlorine ions which are crucial for K2SO4 and NaCl production as impurity levels varied from 0% to 10%. These findings emphasize the negative impact of impurities on K2SO4 and NaCl yields.

An Efficient RI-MP2 Algorithm for Distributed Many-GPU Architectures.

Second-order Møller-Plesset perturbation theory (MP2) using the Resolution of the Identity approximation (RI-MP2) is a widely used method for computing molecular energies beyond the Hartree-Fock mean-field approximation. However, its high computational cost and lack of efficient algorithms for modern supercomputing architectures limit its applicability to large molecules. In this paper, we present the first distributed-memory many-GPU RI-MP2 algorithm explicitly designed to utilize hundreds of GPU accelerators for every step of the computation. Our novel algorithm achieves near-peak performance on GPU-based supercomputers through the development of a distributed memory algorithm for forming RI-MP2 intermediate tensors with zero internode communication, except for a single asynchronous broadcast, and a distributed memory algorithm for the energy reduction step, capable of sustaining near-peak performance on clusters with several hundred GPUs. Comparative analysis shows our implementation outperforms state-of-the-art quantum chemistry software by over 3.5 times in speed while achieving an 8-fold reduction in computational power consumption. Benchmarking on the Perlmutter supercomputer, our algorithm achieves 11.8 PFLOP/s (83% of peak performance) performing and the RI-MP2 energy calculation on a 314-water cluster with 7850 primary and 30,144 auxiliary basis functions in 4 min on 180 nodes and 720 A100 GPUs. This performance represents a substantial improvement over traditional CPU-based methods, demonstrating significant time-to-solution and power consumption benefits of leveraging modern GPU-accelerated computing environments for quantum chemistry calculations.

Effect of non-covalent interactions on the stability and structural properties of 2,4-dioxo-4-phenylbutanoic complex: a computational analysis.

The 2,4-dioxo-4-phenylbutanoic acid (DPBA) is a subject of interest in pharmaceutical research, particularly in developing new drugs targeting viral and bacterial infections. Complexation with metal ions can improve the stability and solubility of organic compounds. The present study uses quantum chemical calculations to explore the structural and electronic results arising from the interaction between the metal cation (Fe2+) and the π-system of DPBA in different solvents. For this purpose, the analyses of atoms in molecules (AIM) and natural bond orbital (NBO) are employed to comprehend the interaction features and the charge delocalization during the process of complexation. The results demonstrate that the strongest/weakest interactions are evident when the complex is situated in non-polar/polar solvents, respectively. In addition, the investigated complex exhibits two intramolecular hydrogen bonds (IMHBs) characterized by the O-H···O motif. The results indicate that the HBs present in the complex fall within the category of weak to medium HBs. Moreover, the O-H···O HBs are influenced by cation-π interactions, which can increase/decrease their strength in polar/non-polar solvents. To enhance understanding of the interactions above, an examination is conducted on various physical properties including the energy gap, electronic chemical potential, chemical hardness, softness, and electrophilicity power. All calculations are conducted within the density functional theory (DFT) using the ωB97XD functional and 6-311 + + G(d,p) basis set. The computations are performed using the quantum chemistry package GAMESS, and the obtained results are visualized by employing the GaussView program.

Optimization of Copper-Ammonia-Sulfate Electrolyte for Maximizing Cu(I):Cu(II) Ratio Using pH and Copper Solubility

An investigation has been carried out to understand the solution chemistry of the Cu-NH−-SO4−2 system, focusing on the effect of pH on the solubility of copper in the solution and maximizing the Cu(I):Cu(II) ratio. A Pourbaix diagram for the Cu-N-S system has also been created using the HSC Chemistry software for a wide range of Cu-NH3 species, unlike most other studies that focused only on Cu(NH3)42+ and Cu(NH3)52+ (Cu(II)) as the dominant species. The Pourbaix diagram demonstrated that the Cu(I) exists as Cu(NH3)2+, while the Cu(II) species are present in the system as Cu(NH3)42+ and Cu(NH3)52+, depending upon the Eh and pH of the solution. Copper precipitation was observed in the electrolyte at pH values less than 8.0, and the precipitation behavior increased as the pH became acidic. The highest Cu(I):Cu(II) ratio was observed at higher pH values of 10.05 due to the higher solubility of copper at higher alkaline pH. The maximum Cu(II) concentration can be achieved at 4.0 M NH4OH and 0.76 M (NH4)2SO4. In the case of low pH, the highest Cu(I):Cu(II) ratio obtained was 0.91 against the 4.0 M and 0.25 M concentrations of NH4OH and (NH4)2SO4, respectively. Meanwhile, at high pH, the maximum Cu(I):Cu(II) ratio was 15.11 against the 0.25 M (NH4)2SO4 and 4.0 M NH4OH. Furthermore, the low pH experiments showed the equilibrium constant (K) K < 1, and the high pH experiments demonstrated K > 1, which justified the lower and higher copper concentrations in the solution, respectively.

ModelHamiltonian: A Python-scriptable library for generating 0-, 1-, and 2-electron integrals.

ModelHamiltonian is a free, open source, and cross-platform Python library designed to express model Hamiltonians, including spin-based Hamiltonians (Heisenberg and Ising models) and occupation-based Hamiltonians (Pariser-Parr-Pople, Hubbard, and Hückel models) in terms of 1- and 2-electron integrals, so that these systems can be easily treated by traditional quantum chemistry software programs. ModelHamiltonian was originally intended to facilitate the testing of new electronic structure methods using HORTON but emerged as a stand-alone research tool that we recognize has wide utility, even in an educational context. ModelHamiltonian is written in Python and adheres to modern principles of software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. While we anticipate that most users will use ModelHamiltonian as a Python library, we include a graphical user interface so that models can be built without programming, based on connectivity/parameters inferred from, for example, a SMILES string. We also include an interface to ChatGPT so that users can specify a Hamiltonian in plain language (without learning ModelHamiltonian's vocabulary and syntax). This article marks the official release of the ModelHamiltonian library, showcasing its functionality and scope.

Thermodynamic analysis of interactions between the components of the charge during the production of metallurgical silicon

The theoretical foundations of the recovery process and the technology of silicon smelting in electric arc furnaces are considered. The results of a study of thermodynamics, reaction kinetics, and mechanisms of the melting process are presented. Production issues are covered: charge preparation, smelting, refining, gas purification. Physicochemical models of the carbothermal process were developed, proposed and tested under industrial conditions (Tau-Ken-Temir LLP), which made it possible to assess the influence of the specified technological parameters of the smelting (chemical composition and loading coefficients of charge components, temperature) on silicon recovery and grade. To obtain the base material, metallurgical grade silicon is used, obtained by melting in ore-thermal furnaces (RTP). In this regard, the research goal is determined, which consists in studying ways to improve the quality (chemical purity) of silicon, and methods for achieving it at this stage of the study - modeling the carbothermal process for producing silicon.The article describes the main mechanism for the recovery of silica in the furnace, presents one of the designs of the furnace and the technological scheme for the production of silicon. The author suggested studying the process of producing metallurgical silicon in RTP using the HSC Chemistry software package. A model of silicon smelting in RTP was formed, which adequately describes the technological process for producing metallurgical silicon.

Predicting Collision-Induced-Dissociation Tandem Mass Spectra (CID-MS/MS) Using Ab Initio Molecular Dynamics.

Compound identification is at the center of metabolomics, usually by comparing experimental mass spectra against library spectra. However, most compounds are not commercially available to generate library spectra. Hence, for such compounds, MS/MS spectra need to be predicted. Machine learning and heuristic models have largely failed except for lipids. Here, quantum chemistry software can be used to predict mass spectra. However, quantum chemistry predictions for collision induced dissociation (CID) mass spectra in LC-MS/MS are rare. We present the CIDMD (Collision-Induced Dissociation via Molecular Dynamics) framework to model CID-based MS/MS spectra. It uses first-principles molecular dynamics (MD) to simulate the physical process of molecular collisions in CID tandem mass spectrometry. First, molecular ions are constructed at specific protonation sites. Using density functional theory, these protonated ions are targeted by argon collider gas atoms at user-specified velocities. Subsequent bond breakages are simulated over time for at least 1,000 fs. Each simulation is repeated multiple times from various collisional directions. Fragmentations are accumulated over those repeated collisions to generate CIDMD in silico mass spectra. Twelve small metabolites (<205 Da) were selected to test the accuracy of this framework in comparison to experimental MS/MS spectra. When testing different protomers, collider velocities, number of simulations, simulation time and impact factor b cutoffs, we yielded 261 predicted mass spectra. These in silico spectra resulted in entropy similarity scores of an average 624 ± 189 for all 261 spectra compared to their corresponding experimental spectra, which improved to 828 ± 77 when using optimal parameters of the most probable protomers for 12 molecules. With increasing molecular mass, higher velocities achieved better results. Similarly, different protomers showed large differences in fragmentation; hence, with increasing numbers of protomers and tautomers, the average CIDMD prediction accuracy decreased. Mechanistic details showed that specific fragment ions can be produced from different protomers via multiple fragmentation pathways. We propose that CIDMD is a suitable tool to predict mass spectra of small metabolites like produced by the gut microbiome.

Predicting pKa Values of Para-Substituted Aniline Radical Cations vs. Stable Anilinium Ions in Aqueous Media

The focus of pKa calculations has primarily been on stable molecules, with limited studies comparing radical cations and stable cations. In this study, we comprehensively investigate models with implicit solvent and explicit water molecules, direct and indirect calculation approaches, as well as methods for calculating free energy, solvation energy, and quasi-harmonic oscillator approximation for para-substituted aniline radical cations (R-PhNH2•+) and anilinium cations (R-PhNH3+) in the aqueous phase. Properly including and positioning explicit H2O molecules in the models is important for reliable pKa predictions. For R-PhNH2•+, precise pKa values were obtained using models with one or two explicit H2O molecules, resulting in a root mean square error (RMSE) of 0.563 and 0.384, respectively, for both the CBS-QB3 and M062X(D3)/ma-def2QZVP methods. Further improvement was achieved by adding H2O near oxygen-containing substituents, leading to the lowest RMSE of 0.310. Predicting pKa values for R-PhNH3+ was more challenging. CBS-QB3 provided an RMSE of 0.349 and the M062X(D3)/ma-def2QZVP method failed to calculate pKa accurately (RMSE &gt; 1). However, by adopting the double-hybrid functional method and adding H2O near the R substituent group, the calculations were significantly improved with an average absolute difference (ΔpKa) of 0.357 between the calculated and experimental pKa values. Our study offers efficient and reliable methods for pKa calculations of R-PhNH2•+ (especially) and R-PhNH3+ based on currently mature quantum chemistry software.

Massively Parallel Computational Chemistry with the Super Instruction Architecture and ACES4.

The task of developing high-performing parallel software must be made easier and more cost-effective in order to fully exploit existing and emerging large-scale computer systems for the advancement of science. The Super Instruction Architecture (SIA) is a parallel programming platform geared toward applications that need to manage large amounts of data stored in potentially sparse multidimensional arrays during calculations. The SIA platform was originally designed for the quantum chemistry software package ACESIII. More recently, the SIA was reimplemented to overcome the limitations in the original ACESIII program. It has now been successfully employed in the new ACES4 quantum chemistry software package. This paper describes the SIA and ACES4 and illustrates their capabilities with some difficult quantum chemistry open-shell coupled-cluster benchmark calculations.

Investigating the kinetics, thermodynamic properties, and mechanisms of high-temperature oxidation in ferrosilicon alloys

Ferrosilicon alloys are widely used as a deoxidizer in steelmaking due to their strong oxygen affinity and high deoxidizing ability. However, compared to other deoxidizers, relatively few studies have been conducted on its kinetics, thermodynamics, and reaction mechanisms. This study aims to investigate the oxidation characteristics of ferrosilicon alloys at different particle sizes, reaction temperatures, and reaction times, and to evaluate their oxidation structure and reaction characteristics. To achieve this, we used the response surface methodology (RSM) to establish a multi-factor model. The results show that reaction time and reaction temperature are the primary factors affecting the oxidation rates, followed by particle size. Wagner’s law was used to verify that the change in oxidation mass follows the parabolic rate law and was discussed in relation to other alloys. Additionally, the application of the volumetric model (VM) and the unreacted core model (URCM) in calculating the activation energy (E) was compared to understand the kinetic characteristics of ferrosilicon alloys more comprehensively. Specifically, when the particle size range increases from 0.075–––0.1 mm to 0.3–––0.5 mm, the estimated E of the VM model increases from 81.82 KJ·mol−1 to 87.84 KJ·mol−1. The URCM model’s result increases from 91.53 KJ·mol−1 to 97.21 KJ·mol−1. Additionally, we evaluated the thermodynamic feasibility of ferrosilicon alloys at temperatures ranging from 273.15 K to 1673.15 K using HSC Chemistry software. This paper describes in detail the oxide layer structure and oxidation reaction process of ferrosilicon alloys and proposes an oxidation reaction mechanism, providing a reliable theoretical basis for the application system of ferrosilicon alloys.

Synergy of Experiment and Broadened Exploration of Ab Initio Calculations for Understanding of Lanthanide-Pentacyanidocobaltate Molecular Nanomagnets and Their Optical Properties.

We present a synergistic experimental-theoretical methodology for the investigation of lanthanide-based single-molecule magnets (SMMs), demonstrated using the example of novel heterometallic molecules incorporating Nd3+/Ce3+ ions combined with three different, rarely explored, pentacyanidocobaltate(III) metalloligands, [CoIII(CN)5(azido/nitrito-N/iodido)]3-. The theoretical part of our approach broadens the exploration of ab initio calculations for lanthanide(III) complexes toward the convenient simulations of such physical characteristics as directional dependences of Helmholtz energy, magnetization, susceptibility, and their thermal and field evolution, as well as light absorption and emission bands. This work was conducted using newly designed SlothPy software (https://slothpy.org). It is introduced as an open-source Python library for simulating various physical properties from first-principles based on results of electronic structure calculations obtained within popular quantum chemistry packages. The computational results were confronted with spectroscopic and ac/dc-magnetic data, the latter analyzed using previously designed relACs software. The combination of experimental and computational methods gave insight into phonon-assisted magnetic relaxation mechanisms, disentangling them from the temperature-independent quantum tunneling of magnetization and emphasizing the role of local-mode processes. This study provides an understanding of the changes in lanthanide(III) magnetic anisotropy introduced with pentacyanidocobaltates(III) modifications, theoretically exploring also potential applications of reported compounds as anisotropy switches or optical thermometers.

Classification of surface waters and groundwater in terms of drinking, agriculture, and industry

In this study, samples were collected from Seydoon River, The Alaa River, Galal Duparan Well, and Seyed Behzad Seepage Well near Seydoon River during the water year 2021-2022 for evaluation by the Khuzestan Water and Power Organization. Following laboratory analysis, Schuler and Wilcox diagrams were created using Aqua and Chemistry software to classify and interpret the water from The Seydoon River, The Alaa River, Duparan Well, and Seyyed Behzad Seepage Well. The objective of this study is to assess and comprehend the water quality of the rivers and wells in the region for drinking, industrial, and agricultural purposes. Overall, the groundwater in the area is deemed to be of medium to unsuitable quality for drinking. The well water is are classified as extremely hard. Furthermore, The Seydoon River and The Alaa River exhibit lower hardness levels compared to the groundwater in the region. in accordance with the Wilcox diagram, both surface and groundwater in the area are suitable in sodium absorption ratio for agricultural use, but they do face salinity issues. The water from The Seydoon River is suitable for industrial purposes, while The Seydoon River, The Alaa River, and Seyyed Behzad Seepage Well water is drinkable, and the water from Duparan Well is prone to forming deposits.

Computational study on the Maillard reactions of glucose and galactose with lysine.

Milk has nutrient-rich but thermal sensitive matrix that undergoes varying degrees of Maillard reaction (MR) at heating conditions. The MR mainly occurs between lysine residues (Lys) and lactose composed of glucose (Glc) and galactose (Gal), which are abundantly sourced from dairy products. In the present study, the MRs of Glc and Gal with Lys at the initial and intermediate stages have been investigated theoretically using density functional theory (DFT) to simulate the gaseous and aqueous phases. Reaction mechanisms have been proposed, and relative energy changes of different steps were calculated according to the total mass balance. The calculations reveal that both Nα- and Nε-amine groups of Lys can react with the carbonyl functional group of Glc and Gal with the similar potential energy profiles, and Gal is more reactive than Glc. However, the barrier in Nε-channel is lower than in Nα-channel, indicating a faster reaction rate through the former channel compared with the latter. The 5-hydroxymethyl-2-furfural (HMF) and derivative are formed under 3-deoxysone route in the intermediate stage. The calculation results are helpful for proposing a reasonable MR mechanism and suggesting possible control methods of the MRs. In this study, different levels of DFT calculations have been conducted to investigate the mechanisms and favorability of generating MR products in Glc-Lys and Gal-Lys models at initial and intermediate stages in the gaseous and aqueous conditions. In order to elucidate the molecular models from the perspectives of chemistry and geometry, DFT calculations were performed by the mean of B3LYP functional at basis sets of 6-311 + + G (d, p) and 6-311 + + G (2df, 2p) with optional solvation settings. To examine the solvation effect, the study further constructed models with solvent H2O and calculated in wB97XD functional with 6-31 + G (d) basis set. All computations were carried out Gaussian 09 suite of quantum chemistry software.

Runtime performance of a GAMESS quantum chemistry application offloaded to GPUs

SummaryComputational chemistry is at the forefront of solving urgent societal problems, such as polymer upcycling and carbon capture. The complexity of modeling these processes at appropriate length and time scales is mainly manifested in the number and types of chemical species involved in the reactions and may require models of several thousand atoms and large basis sets to accurately capture the chemical complexity and heterogeneity in the physical and chemical processes. The quantum chemistry package General Atomic and Molecular Electronic Structure System (GAMESS) has a wide array of methods that can efficiently and accurately treat complex chemical systems. In this work, we have used the GAMESS Effective Fragment Molecule Orbital (EFMO) method for electronic structure calculation of a challenging mesoporous silica nanoparticle (MSN) model surrounded by about 4700 water molecules to investigate the strong scaling and GPU offloading on hybrid CPU‐GPU nodes. Experiments were performed on the Perlmutter platform at the National Energy Research Scientific Computing Center. Good strong scaling and load balancing have been observed on up to 88 hybrid nodes for different settings of the execution parameters for the calculation considered here. When GPUs are oversubscribed by offloading work from multiple CPU processes, using the NVIDIA multi‐process service (MPS) has consistently reduced time to solution and energy consumed. Additionally, for some configuration parameter settings, oversubscription with MPS improved performance by up to 5.8% over the case without oversubscription.

Performance of a Rapid Digital PCR Test for the Detection of Non-Small Cell Lung Cancer (NSCLC) Variants.

Next-generation sequencing is widely used for comprehensive molecular profiling for many cancers including lung cancer. However, the complex workflows and long turnaround times limit its access and utility. ChromaCode's High Definition PCR Non-Small Cell Lung Cancer Panel (HDPCR™ NSCLC Panel) is a low-cost, rapid turnaround, digital polymerase chain reaction assay that is designed to detect variants in nine NSCLC genes listed in National Comprehensive Cancer Network guidelines. This assay uses TaqMan® probe limiting chemistry and proprietary analysis software to enable multi-target detection within a single-color channel. We compared the performance of the HDPCR™ NSCLC Panel against an in-house, laboratory-developed, targeted next-generation sequencing panel used in the Molecular Diagnostics Laboratory at the University of Minnesota Medical Center to detect biomarkers for NSCLC. The overall accuracy of the HDPCR panel was 99.48% (95% confidence interval 99.01-99.76) with a sensitivity of 95.35% (95% confidence interval 88.52-98.72) and a specificity of 99.69% (95% confidence interval 99.29-99.90). The HDPCR wet lab workflow was 4 h, and the time to generate variant calls from raw data using the ChromaCode Cloud was 2 minutes. We demonstrated that the HDPCR™ NSCLC Panel provides timely, comprehensive, and sensitive mutation detection in NSCLC samples with results in less than 24 h.

PyDFT-QMMM: A modular, extensible software framework for DFT-based QM/MM molecular dynamics.

PyDFT-QMMM is a Python-based package for performing hybrid quantum mechanics/molecular mechanics (QM/MM) simulations at the density functional level of theory. The program is designed to treat short-range and long-range interactions through user-specified combinations of electrostatic and mechanical embedding procedures within periodic simulation domains, providing necessary interfaces to external quantum chemistry and molecular dynamics software. To enable direct embedding of long-range electrostatics in periodic systems, we have derived and implemented force terms for our previously described QM/MM/PME approach [Pederson and McDaniel, J. Chem. Phys. 156, 174105 (2022)]. Communication with external software packages Psi4 and OpenMM is facilitated through Python application programming interfaces (APIs). The core library contains basic utilities for running QM/MM molecular dynamics simulations, and plug-in entry-points are provided for users to implement custom energy/force calculation and integration routines, within an extensible architecture. The user interacts with PyDFT-QMMM primarily through its Python API, allowing for complex workflow development with Python scripting, for example, interfacing with PLUMED for free energy simulations. We provide benchmarks of forces and energy conservation for the QM/MM/PME and alternative QM/MM electrostatic embedding approaches. We further demonstrate a simple example use case for water solute in a water solvent system, for which radial distribution functions are computed from 100ps QM/MM simulations; in this example, we highlight how the solvation structure is sensitive to different basis-set choices due to under- or over-polarization of the QM water molecule's electron density.

An improved DIIS method using a versatile residual matrix to accelerate SCF starting from a crude guess.

The minimization of the commutator of the Fock and density matrices as the error matrix in the direct inversion of the iterative subspace (CDIIS) developed by Pulay is a powerful self-consistent field (SCF) acceleration technique for the construction of optimum Fock matrix, if initiated with a fair initial guess. In this work, we present an alternative minimized error matrix to the commutator in the CDIIS, namely the residual or the gradient of the energy-functional for a Slater determinant subject to the orthonormality constraints among orbitals, representing the search for a newly improved Fock matrix in the direction of the residual in the direct inversion of the iterative subspace (RDIIS). Implemented in the computational chemistry package GAMESS, the RDIIS is compared with the standard CDIIS and the second order SCF orbital optimization (SOSCF) for tested molecules started with a crude guess. As a result, the RDIIS stably and efficiently performs the SCF convergence acceleration. Furthermore, the RDIIS is considerably independent on the subspace size with the concentrated linear coefficients accounting proportionally for the Fock matrices close to the current iteration.