High-throughput Computational Screening Research Articles

Computational Exploration of Adsorption-Based Hydrogen Storage in Mg-Alkoxide Functionalized Covalent-Organic Frameworks (COFs): Force-Field and Machine Learning Models.

Hydrogen is a clean-burning fuel that can be converted to other forms. of energy without generating any greenhouse gases. Currently, hydrogen is stored either by compression to high pressure (>700 bar) or cryogenic cooling to liquid form (<23 K). Therefore, it is essential to develop safe, reliable, and energy-efficient storage technology that can store hydrogen at lower pressures and temperatures. In this work, we systematically designed 2902 Mg-alkoxide-functionalized covalent-organic frameworks (COFs) and performed high-throughput (HT) computational screening for hydrogen storage applications at 111, 231, and 296 K. To accurately model the interaction between Mg-alkoxide sites and molecular hydrogen, we performed MP2 calculations to compute the hydrogen binding energy for different types of functionalized models, and the data were subsequently used to fit modified-Morse force field (FF) parameters. Using the developed FF models, we conducted HT grand canonical Monte Carlo (GCMC) simulations to compute hydrogen uptakes for both original and functionalized COFs. The generated data were subsequently used to evaluate the materials' gravimetric and volumetric storage performance at various temperatures (111, 231, and 296 K). Finally, we developed machine learning (ML) models to predict the hydrogen storage performance of functionalized structures based on the features of the original structures. The developed model showed excellent performance with a mean absolute error (MAE) of 0.061 wt % and 0.456 g/L for predicting the gravimetric and volumetric deliverable capacities, enabling a quick evaluation of structures in a hypothetical COF database. The screening results demonstrated that the Mg-alkoxide functionalization yields greater improvements in volumetric H2 storage capacities for COFs with smaller pores compared to those with larger (mesoporous) pores.

Computationally Driven Discovery of T Center-like Quantum Defects in Silicon.

Quantum technologies would benefit from the development of high-performance quantum defects acting as single-photon emitters or spin-photon interfaces. Finding such a quantum defect in silicon is especially appealing in view of its favorable spin bath and high processability. While some color centers in silicon have been emerging in quantum applications, there remains a need to search for and develop new high-performance quantum emitters. By searching a high-throughput computational database of more than 22,000 charged complex defects in silicon, we identify a series of defects formed by a group III element combined with carbon ((A-C)Si with A = B, Al, Ga, In, Tl) and substituting on a silicon site. These defects are analogous structurally, electronically, and chemically to the well-known T center in silicon ((C-C-H)Si), and their optical properties are mainly driven by an unpaired electron on the carbon p orbital. They all emit in the telecom, and some of these color centers show improved properties compared to the T center in terms of computed radiative lifetime, emission efficiency, or smaller optical linewidth. The kinetic barrier computations and previous experimental evidence show that these T center-like defects can be formed through the capture of a diffusing carbon by a substitutional group III atom. We also show that the synthesis of hydrogenated T center-like defects followed by a dehydrogenation annealing step could facilitate the formation of these defects. Our work motivates further studies on the synthesis and control of this new family of quantum defects and demonstrates the use of high-throughput computational screening to discover new color center candidates.

Computationally Driven Discovery and Characterization of SIRT3-Activating Compounds that Fully Recover Catalytic Activity under NAD+ Depletion

Many chronic, age-related disorders could be mitigated by enhancing the activity of enzymes that regulate biochemical signaling pathways, but all known modes of enzyme activation rely on allosteric binding sites, which have only been identified in less than 10% of proteins. Sirtuins (SIRT1-7) are nicotinamide adenine dinucleotide (NAD+)-dependent deacylases playing critical roles in lifespan and age-related diseases. The physiological importance of sirtuins and the reduction in their catalytic activity with the age-related decline in NAD+ levels have stimulated intense interest in designing sirtuin-activating compounds; however, except for substrate-specific allosteric SIRT1 activators, methodologies for rational design of sirtuin-activating compounds are lacking. Here, we introduce methods for the activation of such enzymes that do not rely on allosteric binding sites, and we demonstrate their successful application to the discovery of first-in-class activators of sirtuin enzymes. We establish how all-atom simulations of an enzyme’s active site under the potential of a small molecule modulator can be used to identify molecular properties that achieve desired changes in local enzyme conformational degrees of freedom conducive to the enhancement of catalytic activity. We apply computational high-throughput screening based on this biophysical model for activation of sirtuin enzymes to the major mitochondrial sirtuin SIRT3, which plays a critical role in age-related disorders but does not have a known allosteric site; we thereby identify first-in-class, nonallosteric activators of this enzyme. These compounds are the first reported steady-state activators of the major mitochondrial sirtuin enzyme, and they operate according to a mode of action not shared by any existing drug. Two such compounds can almost double the catalytic efficiency of SIRT3 with respect to NAD+, thus compensating for the loss in SIRT3 activity that occurs due to the age-related decline in NAD+, and they may be developed for therapeutic applications to combat multiple types of age-related disorders. These discoveries establish a foundation for the development of a new class of drugs that function through the activation of enzymes by modulation of local conformational ensembles. Published by the American Physical Society 2024

Finding high-performance MOFs for effective SF6/N2 separation through high-throughput computational screening and machine learning

Abstract Given the rapidly expanding pool of synthesized and hypothetical metal–organic frameworks (MOFs), testing every single material for SF6/N2 separation by iterative experimental methods or computationally demanding molecular simulations is not practical. In this study, we integrated high-throughput computational screening and machine learning (ML) approaches to evaluate SF6/N2 mixture adsorption and separation performances of over 25 000 different types of synthesized and hypothetical MOFs (hypoMOFs), representing the largest set of structures studied for SF6/N2 separation to date. SF6/N2 mixture adsorption data that we produced for synthesized MOFs using molecular simulations were utilized to develop ML models to accurately and quickly predict SF6 and N2 uptakes, SF6/N2 selectivities, SF6 working capacities, adsorbent performance scores, and regenerabilities of both synthesized and hypoMOFs. Results showed the MOF space that we studied exhibits very high SF6/N2 selectivities in the range of 1.8–4204 at 1 bar in addition to high SF6 working capacities between 0.04–5.68 mol kg−1 at an adsorption pressure of 1 bar and desorption pressure of 0.1 bar at room temperature. The top-performing MOF adsorbents for SF6/N2 mixture separation were identified to have Zn, Cu, Ni metals; terphenyl, pyridine, naphthalene linkers; and medium pore sizes. Our comprehensive computational approach offers a highly efficient alternative to brute-force computer simulations by enabling the rapid assessment of the MOF adsorbents for SF6/N2 separation and provides molecular insights into the key structural features of the most promising adsorbents.

Exploiting Metal-Organic Frameworks for Vinylidene Fluoride Adsorption: From Force Field Development, Computational Screening to Machine Learning.

Metal-organic frameworks (MOFs) represent a distinctive class of nanoporous materials with considerable potential across a wide range of applications. Recently, a handful of MOFs has been explored for the storage of environmentally hazardous fluorinated gases (Keasler et al. Science 2023, 381, 1455), yet the potential of over 100,000 MOFs for this specific application has not been thoroughly investigated, particularly due to the absence of an established force field. In this study, we develop an accurate force field for nonaversive hydrofluorocarbon vinylidene fluoride (VDF) and conduct high-throughput computational screening to identify top-performing MOFs with high VDF adsorption capacities. Quantitative structure-property relationships are analyzed via machine learning models on the combinations of geometric, chemical, and topological features, followed by feature importance analysis to probe the effects of these features on VDF adsorption. Finally, from detailed structural analysis via radial distribution functions and spatial densities, we elucidate the significance of different interaction modes between VDF and metal nodes in top-performing MOFs. By synergizing force-field development, computational screening, and machine learning, our findings provide microscopic insights into VDF adsorption in MOFs that will advance the development of new nanoporous materials for high-performance VDF storage or capture.

Orbital magnetization in two-dimensional materials from high-throughput computational screening

Abstract We calculate the orbital magnetization of 822 two-dimensional magnetic materials from the Computational 2D Materials Database (C2DB). For compounds containing 5$d$ elements we find orbital moments of the order of 0.3--0.5 $\mu_\mathrm{B}$, which points to the necessity of including these in any type of magnetic modeling and comparison with experiments. It is also shown that the alignment of orbital moments with respect to the spin largely follows the predictions from Hund's rule and that deviations may be explained by the $d$-band splitting originating from the crystal field - for example in the important case of CrI$_3$. Finally, we show that for certain insulators, Hubbard corrections may lead to large and fully unquenched orbital moments that are pinned to the lattice rather than the spin and that these moments can lead to enormous magnetic anisotropies. Such unquenched ground states are only found from density functional theory calculations that include both Hubbard corrections and self-consistent spin--orbit coupling and largely invalidates the use of the magnetic force theorem for calculating magnetic anisotropies.

High-Throughput Computational Screening of Hydrocarbon Molecules for Long-Wavelength Infrared Imaging

High-Throughput Computational Screening of Hydrocarbon Molecules for Long-Wavelength Infrared Imaging

Van der Waals Electrides.

ConspectusElectrides make up a fascinating group of materials with unique physical and chemical properties. In these materials, excess electrons do not behave like normal electrons in metals or form any chemical bonds with atoms. Instead, they "float" freely in the gaps within the material's structure, acting like negatively charged particles called anions (see the graph). Recently, there has been a surge of interest in van der Waals (vdW) electrides or electrenes in two dimensions. A typical example is layered lanthanum bromide (LaBr2), which can be taken as [La3+(Br1-)2]+•(e-). Each excess free electron is trapped within a hexagonal pore, forming dense dots of electron density. These anionic electrons are loosely bound, giving vdW electrides some unique properties such as ferromagnetism, superconductivity, topological features, and Dirac plasmons. The high density of the free electron makes electrides very promising for applications in thermionic emission, organic light-emitting diodes, and high-performance catalysts.In this Account, we first discuss the discovery of numerous vdW electrides through high-throughput computational screening of over 67,000 known inorganic crystals in Materials Project. A dozen of them have been newly discovered and have not been reported before. Importantly, they possess completely different structural prototypes and properties of anionic electrons compared to widely studied electrides such as Ca2N. Finding these new vdW electrides expands the variety of electrides that can be made in the experiment and opens up new possibilities for studying their unique properties and applications.Then, based on the screened vdW electrides, we delve into their various emerging properties. For example, we developed a new magnetic mechanism specific to atomic-orbital-free ferromagnetism in electrides. We uncover the dual localized and extended nature of the anionic electrons in such electrides and demonstrate the formation of the local moment by the localized feature and the ferromagnetic interaction by the direct overlapping of their extended states. We further show the effective tuning of the magnetic properties of vdW electrides by engineering their structural, electronic, and compositional properties. Besides, we show that the complex interaction between the multiple quantum orderings in vdW electrides leads to many interesting properties including valley polarization, charge density waves, a topological property, a superconducting property, and a thermoelectrical property.Moreover, we discuss strategies to leverage the unique intrinsic properties of vdW electrides for practical applications. We show that these properties make vdW electrides potential candidates for advanced applications such as spin-orbit torque memory devices, valleytronic devices, K-ion batteries, and thermoelectricity. Finally, we discuss the current challenges and future perspectives for research using these emerging materials.

Transition-metal-doped biphenylene for enhanced CO detection: A high-throughput first-principles study

Our study examines the CO detection capabilities of pristine biphenylene (BP) and transition-metal-doped BP (M–BPx, x = 1 or 2), a novel two-dimensional nano-carbon material. Despite BP’s potential in toxic gas detection, its low CO adsorption energy (Eads) of 0.134 eV limits its application. Utilizing a combined strategy of high-throughput computational screening and DFT technique, we accurately identified the ground state configurations of CO-adsorbed M–BPx (M = Sc, Ti, Fe, Co, Ni, and Zn) and confirmed M doping significantly boosts BP’s CO adsorption ability, with Eads values ranging from 2.809 to 8.206 eV. This improvement is supported by ab initio molecular dynamics simulations and ionic d-p bonding interactions observed in electron localization functions, densities of state (DOS), and bond population analysis. Additionally, CO adsorption induces notable DOS changes in the M–BPx systems, validating the potential of M–BPx as suitable materials for CO detection. Notably, Zn-BP2 show notable work function shift after CO adsorption (0.36 eV), outperforming pristine BP’s 0.30 eV shift, and favorable recovery time (8.940 s). These shifts underscore the potential of Zn-BP2 for work function-based high-temperature CO sensor, marking it as a promising material for sensitive CO detection.

Magnetic order in the computational 2D materials database (C2DB) from high throughput spin spiral calculations

We report high throughput computational screening for magnetic ground state order in 2D materials. The workflow is based on spin spiral calculations and yields the magnetic order in terms of a two-dimensional ordering vector Q. We then include spin-orbit coupling to extract the easy and hard axes for collinear structures and the orientation of spiral planes in non-collinear structures. Finally, for all predicted ferromagnets we compute the Dzyaloshinskii-Moriya interactions and determine whether or not these are strong enough to overcome the magnetic anisotropy and stabilise a chiral spin spiral ground state. We find 58 ferromagnets, 21 collinear anti-ferromagnets, and 85 non-collinear ground states of which 15 are chiral spin spirals driven by Dzyaloshinskii-Moriya interactions. The results show that non-collinear order is in fact as common as collinear order in these materials and emphasise the need for detailed investigation of the magnetic ground state when reporting magnetic properties of new materials.

High-throughput computational screening for optimal solvents in methyl 2-hydroxyisobutyrate-water separation

In the semiconductor industry, the efficacy of methyl 2-hydroxyisobutyrate (HBM) as a photoresist thinner is crucial but often compromised by water contamination. Additionally, the manufacturing process tends to form an azeotrope with water, rendering traditional distillation methods inefficient for purification. This study introduces a novel computational screening approach to identify optimal solvents for the liquid-liquid extraction of HBM, offering a sustainable alternative to conventional distillation. Our three-step sequential screening protocol utilizes molecular dynamics simulations to evaluate intermolecular interaction energy, partition coefficient, and environmental, health, and safety (EHS) parameters. Initially, a comprehensive database of 4919 solvents was screened using molecular dynamics simulations to assess interaction energies and partition coefficients. Solvents that met these energy criteria were further evaluated for their EHS profiles. This process identified seven solvents that optimize extraction efficiency while adhering to stringent environmental and safety standards. Our approach not only enhances the purity and performance of HBM in semiconductor manufacturing but also presents a green, cost-effective solution for other industrial chemical processes requiring refined solvent selection.

Computational Simulations of Metal-Organic Frameworks to Enhance Adsorption Applications.

Metal-organic frameworks (MOFs), renowned for their exceptional porosity and crystalline structure, stand at the forefront of gas adsorption and separation applications. Shortly after their discovery through experimental synthesis, computational simulations quickly become an important method in broadening the use of MOFs by offering deep insights into their structural, functional, and performance properties. This review specifically addresses the pivotal role of molecular simulations in enlarging the molecular understanding of MOFs and enhancing their applications, particularly for gas adsorption. After reviewing the historical development and implementation of molecular simulation methods in the field of MOFs, high-throughput computational screening (HTCS) studies used to unlock the potential of MOFs in CO2 capture, CH4 storage, H2 storage, and water harvesting are visited and recent advancements in these adsorption applications are highlighted. The transformative impact of integrating artificial intelligence with HTCS on the prediction of MOFs' performance and directing the experimental efforts on promising materials is addressed. An outlook on current opportunities and challenges in the field to accelerate the adsorption applications of MOFs is finally provided.

Unraveling the separation mechanism of gas mixtures in MOFs by combining the breakthrough curve with machine learning and high-throughput calculation

In the field of metal–organic frameworks (MOFs) screening studies, the batch calculation of the mixed gas breakthrough time difference (ΔTi) in MOFs and its intricate correlation with various descriptors remain underexplored. This research undertook batch calculations of the breakthrough curves (BC) for different gases within a simulated natural gas environment, designating ΔTi as the performance metric for MOFs in gas separation. The separation performance of computation-ready experimental MOFs for CH4/C2H6 and CH4/CO2 mixtures was analyzed in depth utilizing machine learning (ML)-assisted high-throughput computational screening (HTCS) techniques. Then, five ML algorithms were used to quantify the relationship between MOF descriptors and performance, and the effect of the metal center site on the separation performance was further explored. Ultimately, the top ten MOFs were selected for each system. Combining HTCS, ML, and BC, this work provides fresh insights for understanding and designing MOFs with customized adsorption and separation properties.

Artificial Intelligence and High-Throughput Computational Workflows Empowering the Fast Screening of Metal-Organic Frameworks for Hydrogen Storage.

Metal-organic frameworks (MOFs) are one of the most promising hydrogen-storing materials due to their rich specific surface area, adjustable topological and pore structures, and modified functional groups. In this work, we developed automatically parallel computational workflows for high-throughput screening of ∼11,600 MOFs from the CoRE database and discovered 69 top-performing MOF candidates with work capacity greater than 1.00 wt % at 298.5 K and a pressure swing between 100 and 0.1 bar, which is at least twice that of MOF-5. In particular, ZITRUP, OQFAJ01, WANHOL, and VATYIZ showed excellent hydrogen storage performance of 4.48, 3.16, 2.19, and 2.16 wt %. We specifically analyzed the relationship between pore-limiting diameter, largest cavity diameter, void fraction, open metal sites, metal elements or nonmetallic atomic elements, and deliverable capacity and found that not only geometrical and physical features of crystalline but also chemical properties of adsorbate sites determined the H2 storage capacity of MOFs at room temperature. It is highlighted that we first proposed the modified crystal graph convolutional neural networks by incorporating the obtained geometrical and physical features into the convolutional high-dimensional feature vectors of period crystal structures for predicting H2 storage performance, which can improve the prediction accuracy of the neural network from the former mean absolute error (MAE) of 0.064 wt % to the current MAE of 0.047 wt % and shorten the consuming time to about 10-4 times of high-throughput computational screening. This work opens a new avenue toward high-throughput screening of MOFs for H2 adsorption capacity, which can be extended for the screening and discovery of other functional materials.

Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal-Organic Framework Space.

Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal-organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs.

Rapid Mining of Fast Ion Conductors via Subgraph Isomorphism Matching.

The rapidly evolving field of inorganic solid-state electrolytes (ISSEs) has been driven in recent years by advances in data-mining techniques, which facilitates the high-throughput computational screening for candidate materials in the databases. The key to the mining process is the selection of critical features that underline the similarity of a material to an existing ISSE. Unfortunately, this selection is generally subjective and frequently under debate. Here we propose a subgraph isomorphism matching method that allows an objective evaluation of the similarity between two compounds according to the topology of the local atomic environment. The matching algorithm has been applied to discover four structure types that are highly analogous to the LiTi2(PO4)3 NASICON prototype. We demonstrate that the local atomic environments similar to LiTi2(PO4)3 endow these four structures with favorable Li diffusion tunnels and ionic conductivity on par with those of the prototype. By further taking into account the electronic structure and electrochemical stability window, 13 compounds are identified to be potential ISSEs. Our findings not only offer a promising approach toward rapid mining of fast ion conductors without limitation in the compositional range but also reveal insights into the design of ISSEs according to the topology of their framework structures.

Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery

AbstractThis review exploits the crucial role of computational methods in discovering and optimizing materials for redox flow batteries (RFBs). Integration of high‐throughput computational screening (HTCS) and machine learning (ML) accelerates materials discovery, guided by algorithms categorizing RFBs. A collaborative exploration, spanning macroscopic to mesoscopic scales, combines quantum machine learning with reinforcement learning, transfer learning, time series analysis, Bayesian optimization, active learning and various generative models. The collaborative integration of ML with computational techniques and experimental methods, anchored in experimentally validated Density Functional Theory (DFT) calculations and molecular dynamics (MD) simulations, proves indispensable for cost‐effective RFBs. Data collection and feature engineering are explored, emphasizing the integration of optimization goals and precise data collection within the ML framework. Feature analysis importance is highlighted, utilizing methods such as the filter, embedded, wrapper and deep learning methods for efficient energy materials exploration. Computational perspectives on materials features and operating conditions encompass membrane characteristics, fluid dynamics, temperature dependence and pressure sensitivity. Time‐dependent features and ML‐generated insights are crucial for understanding cycling performance intricacies, providing a comprehensive understanding of RFB materials.

Quantifying the Uncertainty of Force Field Selection on Adsorption Predictions in MOFs.

Comparisons between simulated and experimental adsorption isotherms in MOFs are fraught with challenges. On the experimental side, there is significant variation between isotherms measured on the same system, with a significant percentage (∼20%) of published data being considered outliers. On the simulation side, force fields are often chosen "off-the-shelf" with little or no validation. The effect of this choice on the reliability of simulated adsorption predictions has not yet been rigorously quantified. In this work, we fill this gap by systematically quantifying the uncertainty arising from force field selection on adsorption isotherm predictions. We choose methane adsorption, where electrostatic interactions are negligible, to independently study the effect of the framework Lennard-Jones parameters on a series of prototypical materials that represent the most widely studied MOF "families". Using this information, we compute an adsorption "consensus isotherm" from simulations, including a quantification of uncertainty, and compare it against a manually curated set of experimental data from the literature. By considering many experimental isotherms measured by different groups and eliminating outliers in the data using statistical analysis, we conduct a rigorous comparison that avoids the pitfalls of the standard approach of comparing simulation predictions to a single experimental data set. Our results show that (1) the uncertainty in simulated isotherms can be as large as 15% and (2) standard force fields can provide reliable predictions for some systems but can fail dramatically for others, highlighting systematic shortcomings in those models. Based on this, we offer recommendations for future simulation studies of adsorption, including high-throughput computational screening of MOFs.

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.