High-throughput Computational Screening Research Articles

Combining computational screening and machine learning to explore MOFs and COFs for methane purification

Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have great potential to be used as porous adsorbents and membranes to achieve high-performance methane purification. Although the continuous increase in the number and diversity of MOFs and COFs is a great opportunity for the discovery of novel adsorbents and membranes with superior performances, evaluating such a vast number of materials in the quickest and most effective manner requires the development of computational approaches. High-throughput computational screening based on molecular simulations has been extensively used to identify the most promising MOFs and COFs for methane purification. However, the enormous and ever-growing material space necessitates more efficient approaches in terms of time and effort. Combining data science with molecular simulations has recently accelerated the discovery of optimal MOF and COF materials for methane purification and revealed the hidden structure–performance relationships. In this perspective, we highlighted the recent developments in combining high-throughput molecular simulations and machine learning to accurately identify the most promising MOF and COF adsorbents and membranes among thousands of candidates for separating methane from other gases including acetylene, carbon dioxide, helium, hydrogen, and nitrogen. After providing a brief overview of the topic, we reviewed the pioneering contributions in the field and discussed the current opportunities and challenges that we need to direct our efforts for the design and discovery of adsorbent and membrane materials.

Alkali-Ion-Assisted Activation of ε-VOPO4 as a Cathode Material for Mg-Ion Batteries.

Rechargeable multivalent-ion batteries are attractive alternatives to Li-ion batteries to mitigate their issues with metal resources and metal anodes. However, many challenges remain before they can be practically used due to the low solid-state mobility of multivalent ions. In this study, a promising material identified by high-throughput computational screening is investigated, ε-VOPO4, as a Mg cathode. The experimental and computational evaluation of ε-VOPO4 suggests that it may provide an energy density of >200Whkg-1 based on the average voltage of a complete cycle, significantly more than that of well-known Chevrel compounds. Furthermore, this study finds that Mg-ion diffusion can be enhanced by co-intercalation of Li or Na, pointing at interesting correlation dynamics of slow and fast ions.

High-Throughput Computational Screening of Novel Two-Dimensional Covalent Organic Frameworks for Efficient Photocatalytic Overall Water Splitting.

The pursuit of efficient photocatalysts toward photocatalytic water splitting has attracted wide attention. However, the low efficiency of photocatalytic reactions due to the rapid electron-hole recombination and the time-consuming searching process hinder the development of high-performance photocatalysts. Here, we proposed a data-driven screening procedure for covalent organic frameworks (COFs) as overall solar water-splitting photocatalysts. Based on a COF database through assembling different Cores and Linkers, three COFs are predicted to be efficient photocatalysts for overall solar water splitting after high-throughput computational screening. We found that the photogenerated electrons and holes are well separated on single COF photocatalysts without material engineering, and both hydrogen and oxygen evolution reactions can occur spontaneously on the three screened COFs under visible light radiation. This kind of novel COF screened by a data-driven screening procedure offers new perspectives for advancing efficient photocatalysts.

Machine Learning-Accelerated High-Throughput Computational Screening: Unveiling Bimetallic Nanoparticles with Peroxidase-Like Activity.

Bimetallic nanoparticles (NPs) with peroxidase-like (POD-like) activity play a crucial role in biosensing, disease treatment, environmental management, and other fields. However, their development is impeded by a vast range of tunable properties in components and structures, making the establishment of structure-effect relationships and the discovery of active materials challenging. Addressing this, we established robust scaling relationships by meticulously analyzing the catalytic reaction networks of pure metal NPs, which laid the volcano-shaped correlation between the activity and O* adsorption energy. Utilizing these relationships, we introduced an innovative and versatile descriptor of the NPs, which was then integrated into a machine learning-accelerated high-throughput computational workflow, significantly boosting the predictive accuracy for the POD-like activity of bimetallic NPs. Our methodological approach enabled the successful prediction of activities for 1260 bimetallic NPs, leading to the identification of several highly effective catalysts. Furthermore, we distilled several strategies for designing efficient bimetallic NPs based on our screening results.

Transfer learning-assisted computational screening of metal-organic frameworks and covalent-organic frameworks for the separation of Xe/Kr noble gas

The adsorption separation of xenon/krypton (Xe/Kr) by porous materials has gained significant attention in industrial development. Metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs) featuring permanent porosity, ultra-high surface area, and tunable pore size enable efficient separation of Xe/Kr, which promising structures can be identified by high-throughput computational screening and machine learning. However, the asymmetric development of the structure database between MOFs and other porous materials (including COFs) led to a data imbalance that challenged screening and machine learning for other porous materials, especially for computation-ready experimental (CoRE) databases. Transfer learning (TL) can address this issue by extracting the information from MOFs to improve generalization for other materials. In this work, geometric, energy, and chemical-based descriptors were obtained from MOFs and COFs to train TL models. It was demonstrated that TL with only 300 data sets of hypothetical COFs being used can produce satisfactory accuracy (R2 > 0.85) by predicting CoRE COFs based on CoRE MOFs. Furthermore, we conducted a feature importance analysis that suggested the energy descriptor plays a dominant role in predicting the Xe/Kr separation performance of the ensemble algorithm, whereas the chemical descriptor is an important descriptor for the deep neural network. This study provides an alternative transfer learning route to predict the Xe/Kr separation performance of nanoporous materials database with limited structures.

Two-dimensional altermagnets from high throughput computational screening: Symmetry requirements, chiral magnons, and spin-orbit effects

We present a high throughput computational search for altermagnetism in two-dimensional (2D) materials based on the Computational 2D Materials Database (C2DB). We start by showing that the symmetry requirements for altermagnetism in 2D are somewhat more strict compared to bulk materials and applying these yields a total of seven altermagnets in the C2DB. The collinear ground state in these monolayers is verified by spin spiral calculations using the generalized Bloch theorem. We focus on four d-wave altermagnetic materials belonging to the P21′/c′ magnetic space group—RuF4, VF4, AgF2, and OsF4. The first three of these are known experimentally as van der Waals bonded bulk materials and are likely to be exfoliable from their bulk parent compounds. We perform a detailed analysis of the electronic structure and non-relativistic spin splitting in k-space exemplified by RuF4. The magnon spectrum of RuF4 is calculated from the magnetic force theorem, and it is shown that the symmetries that enforce degenerate magnon bands in anti-ferromagnets are absent in altermagnets and give rise to the non-degenerate magnon spectrum. We then include spin–orbit effects and show that these will dominate the splitting of magnons in RuF4. Finally, we provide an example of i-wave altermagnetism in the 2H-phase of FeBr3.

A substitutional quantum defect in WS2 discovered by high-throughput computational screening and fabricated by site-selective STM manipulation

Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS2, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (CoS0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}_{{{{{{{{\\rm{S}}}}}}}}}^{0}$$\\end{document}) and fabricate it with scanning tunneling microscopy (STM). The CoS0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}_{{{{{{{{\\rm{S}}}}}}}}}^{0}$$\\end{document} electronic structure measured by STM agrees with first principles and showcases an attractive quantum defect. Our work shows how HT computational screening and nanoscale synthesis routes can be combined to design promising quantum defects.

Data-Driven Discovery of Gas-Selective Organic Linkers in Metal-Organic Frameworks for the Separation of Ethylene and Ethane.

Metal-organic frameworks (MOFs) are potential candidates for gas-selective adsorbents for the separation of an ethylene/ethane mixture. To accelerate material discovery, high-throughput computational screening is a viable solution. However, classical force fields, which were widely employed in recent studies of MOF adsorbents, have been criticized for their failure to cover complicated interactions such as those involving π electrons. Herein, we demonstrate that machine learning force fields (MLFFs) trained on quantum-chemical reference data can overcome this difficulty. We have constructed a MLFF to accurately predict the adsorption energies of ethylene and ethane on the organic linkers of MOFs and discovered that the π electrons from both the ethylene molecule and the aromatic rings in the linkers could substantially influence the selectivity for gas adsorption. Four kinds of MOF linkers are identified as having promise for the separation of ethylene and ethane, and our results could also offer a new perspective on the design of MOF building blocks for diverse applications.

High-throughput screening of dual atom catalysts for oxygen reduction and evolution reactions and rechargeable zinc-air battery

We provide the rational design of dual atom catalysts (DACs) supported on nitrogen-doped graphene for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) through high-throughput computational screening of M1M2N6-DAC systems, where M1 and M2 represent Fe, Co, Ni, Ru, Rh, Pd, Os, Ir, or Pt metals. We predict that FeRuN6-DAC at the summit of the volcano plot exhibits a low theoretical ORR overpotential (ηORR) of 0.24 V and a low theoretical OER overpotential (ηOER) of 0.19 V. The low ηORR and ηOER result from the catalytic performance of the Fe site being tuned to electronic properties that facilitate adsorption and desorption of the OH* intermediate. Inspired by these hybrid density functional theory (DFT) computational and machine learning (ML) results, we synthesized FeRuN6-DAC, FeN4-SAC, and RuN4-SAC and characterized them using X-ray photoelectron spectroscopy (XPS), X-ray absorption spectroscopy (XAS), scanning transmission electron microscopy (STEM), and in-situ electron spin resonance (ESR). Our in-situ ESR spectroscopy signifies that the spin of the Fe active site increases with increasing applied potential due to the increase in the concentration of OH* intermediate on Fe. We verified experimentally the predicted catalytic performances, finding that FeRuN6-DAC leads to an experimental ORR overpotential of 0.29 V with a Tafel slope of 104 mVdec−1 and an OER overpotential of 0.27 V with a Tafel slope of 124 mVdec−1. The rechargeable Zinc-air battery setup was fabricated with FeRuN6-DAC in place of the cathode, showing a maximum power density of 0.45 W/cm2 at the current density of 0.44 A/cm2 and good stability after 120 cycles. According to our findings, we demonstrate that DFT-guided strategies are useful for designing advanced DACs applicable to ORR, OER, and Zinc-air battery applications.

Exploring the Potential of Metal-Organic Frameworks for Cryogenic Helium-Based Gas Gap Heat Switches via High-Throughput Computational Screening.

With the advantages of a long lifetime and high reliability, gas gap heat switches (GGHSs) are attractive in many thermal management applications, especially in space-borne cryogenic systems. The performance of a GGHS is significantly affected by the adsorption characteristics of the adsorbent in the sorption pump. Compared with the commonly used adsorbent in the GGHSs (activated carbon), metal-organic frameworks (MOFs) have larger surface areas, higher pore volumes, and exceptional tunability, which motivates this study to explore their potential for application in cryogenic GGHSs. To this end, two performance metrics, the required volume of adsorbent (vsor) and total input heat (qtot), were computed for about 6000 MOFs via molecular simulations and compared with those of activated carbon. It is found that over 2300 MOFs possess a smaller vsor than activated carbon, and the smallest vsor of MOFs is about 12.7% of that of activated carbon. vsor and qtot generally change in the same direction, which implies it is possible to reduce both parameters simultaneously by choosing a suitable MOF. Structure-performance analysis reveals that 1/vsor consistently increases first and then decreases with pore limiting diameter, largest cavity diameter, available pore volume, accessible surface area, helium void fraction, and bulk density. Descriptor ranges corresponding to high-performing MOFs were identified based on Precision-Recall analysis. Notably, Zr-containing MOFs are particularly likely to have smaller vsor values than activated carbon. It is anticipated that the promising MOFs identified by this study will motivate further experimental investigations, and the insights into structure-performance relationships can serve to guide the rational design of novel MOF candidates for GGHSs.

Halide Superionic Conductors with Non-Close-Packed Anion Frameworks.

Halide superionic conductors (SICs) are drawing significant research attention for their potential applications in all-solid-state batteries. A key challenge in developing such SICs is to explore and design halide structural frameworks that enable rapid ion movement. In this work, we show that the close-packed anion frameworks shared by traditional halide ionic conductors face intrinsic limitations in fast ion conduction, regardless of structural regulation. Beyond the close-packed anion frameworks, we identify that the non-close-packed anion frameworks have great potential to achieve superionic conductivity. Notably, we unravel that the non-close-packed UCl3-type framework exhibit superionic conductivity for a diverse range of carrier ions, including Li+, Na+, K+, and Ag+, which are validated through both ab initio molecular dynamics simulations and experimental measurements. We elucidate that the remarkable ionic conductivity observed in the UCl3-type framework structure stems from its significantly more distorted site and larger diffusion channel than its close-packed counterparts. By employing the non-close-packed anion framework as the key feature for high-throughput computational screening, we also identify LiGaCl3 as a promising candidate for halide SICs. These discoveries provide crucial insights for the exploration and design of novel halide SICs.

High-Throughput Computational Screening of Solid-Binding Peptides.

Inspired by biomineralization, a naturally occurring, protein-facilitated process, solid-binding peptides (SBPs) have gained much attention for their potential to fabricate various shaped nanocrystals and hierarchical nanostructures. The advantage of SBPs over other traditionally used synthetic polymers or short ligands is their tunable interaction with the solid material surface via carefully programmed sequence and being solution-dependent simultaneously. However, designing a sequence with targeted binding affinity or selectivity often involves intensive processes such as phage display, and only a limited number of sequences can be identified. Other computational efforts have also been introduced, but the validation process remains prohibitively expensive once a suitable sequence has been identified. In this paper, we present a new model to rapidly estimate the binding free energy of any given sequence to a solid surface. We show how the overall binding of a polypeptide can be estimated from the free energy contribution of each residue based on the statistics of the thermodynamically stable structure ensemble. We validated our model using five silica-binding peptides of different binding affinities and lengths and showed that the model is accurate and robust across a wider range of chemistries and binding strengths. The computational cost of this method can be as low as 3% of the commonly used enhanced sampling scheme for similar studies and has a great potential to be used in high-throughput algorithms to obtain larger training data sets for machine learning SBP screening.

High-throughput virtual search of small molecules for controlling the mechanical stability of human CD4

Protein mechanical stability determines the function of a myriad of proteins, especially proteins from the extracellular matrix. Failure to maintain protein mechanical stability may result in diseases and disorders such as cancer, cardiomyopathies, or muscular dystrophy. Thus, developing mutation-free approaches to enhance and control the mechanical stability of proteins using pharmacology-based methods, may have important implications in drug development and discovery. Here, we present the first approach that employs computational High-Throughput Virtual Screening (HTVS) and Molecular Docking to search for small-molecules in chemical libraries that function as mechano-regulators of the stability of human CD4, receptor of HIV-1. Using single-molecule force spectroscopy we prove that these small-molecules can increase the mechanical stability of CD4D1D2 domains over 4-fold in addition to modifying the mechanical unfolding pathways. Our experiments demonstrate that chemical libraries are a source of mechanoactive molecules and that drug discovery approaches provide the foundation of a new type of molecular function, i.e., mechano-regulation, paving the way towards mechanopharmacology.

High-throughput computational screening of MOF adsorbents for efficient propane capture from air and natural gas mixtures.

In this study, we used a high-throughput computational screening approach to examine the potential of metal-organic frameworks (MOFs) for capturing propane (C3H8) from different gas mixtures. We focused on Quantum MOF (QMOF) database composed of both synthesized and hypothetical MOFs and performed Grand Canonical Monte Carlo (GCMC) simulations to compute C3H8/N2/O2/Ar and C3H8/C2H6/CH4 mixture adsorption properties of MOFs. The separation of C3H8 from air mixture and the simultaneous separation of C3H8 and C2H6 from CH4 were studied for six different adsorption-based processes at various temperatures and pressures, including vacuum-swing adsorption (VSA), pressure-swing adsorption (PSA), vacuum-temperature swing adsorption (VTSA), and pressure-temperature swing adsorption (PTSA). The results of molecular simulations were used to evaluate the MOF adsorbents and the type of separation processes based on selectivity, working capacity, adsorbent performance score, and regenerability. Our results showed that VTSA is the most effective process since many MOFs offer high regenerability (>90%) combined with high C3H8 selectivity (>7 × 103) and high C2H6 + C3H8 selectivity (>100) for C3H8 capture from air and natural gas mixtures, respectively. Analysis of the top MOFs revealed that materials with narrow pores (<10Å) and low porosities (<0.7), having aromatic ring linkers, alumina or zinc metal nodes, typically exhibit a superior C3H8 separation performance. The top MOFs were shown to outperform commercial zeolite, MFI for C3H8 capture from air, and several well-known MOFs for C3H8 capture from natural gas stream. These results will direct the experimental efforts to the most efficient C3H8 capture processes by providing key molecular insights into selecting the most useful adsorbents.

Topological Data Analysis Combined with High-Throughput Computational Screening of Hydrophobic Metal–Organic Frameworks: Application to the Adsorptive Separation of C3 Components

The shape and topology of pores have significant impacts on the gas storage properties of nanoporous materials. Metal–organic frameworks (MOFs) are ideal materials with which to tailor to the needs of specific applications, due to properties such as their tunable structure and high specific surface area. It is, therefore, particularly important to develop descriptors that accurately identify the topological features of MOF pores. In this work, a topological data analysis method was used to develop a topological descriptor, based on the pore topology, which was combined with the Extreme Gradient Boosting (XGBoost) algorithm to predict the adsorption performance of MOFs for methane/ethane/propane. The final results show that this descriptor can accurately predict the performance of MOFs, and the introduction of the topological descriptor also significantly improves the accuracy of the model, resulting in an increase of up to 17.55% in the R2 value of the model and a decrease of up to 46.1% in the RMSE, compared to commonly used models that are based on the structural descriptor. The results of this study contribute to a deeper understanding of the relationship between the performance and structure of MOFs and provide useful guidelines and strategies for the design of high-performance separation materials.

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

High-throughput computational screening for identification of potential hits against bacterial Acriflavine resistance protein B (AcrB) efflux pump

Antibiotic resistance is a pressing global health challenge, driven in part by the remarkable efflux capabilities of efflux pump in AcrB (Acriflavine Resistance Protein B) protein in Gram-negative bacteria. In this study, a multi-approached computational screening strategy encompassing molecular docking, In silico absorption, distribution, metabolism, excretion and toxicity (ADMET) analysis, druglikeness assessment, molecular dynamics simulations and density functional theory studies was employed to identify novel hits capable of acting against AcrB-mediated antibiotic resistance. Ligand library was acquired from the COCONUT database. Performed computational analyses unveiled four promising hit molecules (CNP0298667, CNP0399927, CNP0321542 and CNP0269513). Notably, CNP0298667 exhibited the highest negative binding affinity of −11.5 kcal/mol, indicating a possibility of strong potential to disrupt AcrB function. Importantly, all four hits met stringent druglikeness criteria and demonstrated favorable in silico ADMET profiles, underscoring their potential for further development. MD simulations over 100 ns revealed that the CNP0321542-4DX5 and CNP0269513-4DX5 complexes formed robust and stable interactions with the AcrB efflux pump. The identified hits represent a promising starting point for the design and optimization of novel therapeutics aimed at combating AcrB-mediated antibiotic resistance in Gram-negative bacteria. Communicated by Ramaswamy H. Sarma

High-throughput computational screening and in vitro evaluation identifies 5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl) phenyl]-1H-isoindole-1,3(2H)-dione (C3), as a novel EGFR-HER2 dual inhibitor in gastric tumors.

Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation, adhesion, angiogenesis, and metastasis. Conventional therapies are ineffective due to the intra-tumoral heterogeneity and concomitant genetic mutations. Hence, dual inhibition strategies are recommended to increase potency and reduce cytotoxicity. In this study, we have conducted computational high-throughput screening of the ChemBridge library followed by in vitro assays and identified novel selective inhibitors that have a dual impediment of EGFR/HER2 kinase activities. Diversity-based High-throughput Virtual Screening (D-HTVS) was used to screen the whole ChemBridge small molecular library against EGFR and HER2. The atomistic molecular dynamic simulation was conducted to understand the dynamics and stability of the protein-ligand complexes. EGFR/HER2 kinase enzymes, KATOIII, and Snu-5 cells were used for in vitro validations. The atomistic Molecular Dynamics simulations followed by solvent-based Gibbs binding free energy calculation of top molecules, identified compound C3 (5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl) phenyl]-1H-isoindole-1,3(2H)-dione) to have a good affinity for both EGFR and HER2. The predicted compound, C3, was promising with better binding energy, good binding pose, and optimum interactions with the EGFR and HER2 residues. C3 inhibited EGFR and HER2 kinases with IC50 values of 37.24 and 45.83 nM, respectively. The GI50 values of C3 to inhibit KATOIII and Snu-5 cells were 84.76 and 48.26 nM, respectively. Based on these findings, we conclude that the identified compound C3 showed a conceivable dual inhibitory activity on EGFR/HER2 kinase, and therefore can be considered as a plausible lead-like molecule for treating gastric cancers with minimal side effects, though testing in higher models with pharmacokinetic approach is required.

High-throughput computational materials screening of transition metal peroxides.

Semiconductor materials of abnormal stoichiometric ratio often exhibit unique properties, yet it is still a challenge to determine the structures of such materials in an efficient way. Herein, we propose a method for structurally biased screening according to the coordination numbers and the numbers of Wyckoff positions, balancing the atom local environment and the global symmetry of structures. Based on first-principles calculations, we have predicted two metastable peroxides P21/c-ScO2 and Pmmn-TiO3 with more than six coordination points. For these two structures, the most stable intrinsic defect is the oxygen vacancy (VO) at the peroxide anion (O2-2), which induces the absence of antibonding orbital formed by O2-2 near the valence band maximum. With the introduction of VO, the decrease of coordination numbers leads to charge recombination, and results in the appearance of an ordered phase TiO2.5 with stronger Ti-O orbital hybridization. The proposed method presents a promising and feasible approach for the screening of novel compounds.

High throughput screening of pure silica zeolites for CF4 capture from electronics industry gas.

The capture and separation of CF4 from CF4/N2 mixture gas is a crucial issue in the electronics industry, as CF4 is a commonly used etching gas and the ratio of CF4 to N2 directly affects process efficiency. Utilizing high-throughput computational screening techniques and grand canonical Monte Carlo (GCMC) simulations, we comprehensively screened and assessed 247 types of pure silicon zeolite materials to determine their adsorption and separation performance for CF4/N2 mixtures. Based on screening, the relationships between the structural parameters and adsorption and separation properties were meticulously investigated. Four indicators including adsorption selectivity, working capacity, adsorbent performance score (APS), and regenerability (R%) were used to evaluate the performance of adsorbents. Based on the evaluation, we selected the top three best-performing zeolite structures for vacuum swing adsorption (LEV, AWW and ESV) and pressure swing adsorption (AVL, ZON, and ERI) processes respectively. Also, we studied the preferable adsorption sites of CF4 and N2 in the selected zeolite structures through centroid density distributions at the molecule level. We expect the study may provide some valuable guidance for subsequent experimental investigations on adsorption and separation of CF4/N2.