High-throughput Density Functional Theory Calculations Research Articles

High-Throughput Screening and Prediction of Nucleophilicity of Amines Using Machine Learning and DFT Calculations.

Nucleophilic index (NNu) as a significant parameter plays a crucial role in screening of amine catalysts. Indeed, the quantity and variety of amines are extensive. However, only limited amines exhibit an NNu value exceeding 4.0 eV, rendering them potential nucleophiles in chemical reactions. To address this issue, we proposed a computational method to quickly identify amines with high NNu values by using Machine Learning (ML) and high-throughput Density Functional Theory (DFT) calculations. Our approach commenced by training ML models and the exploration of Molecular Fingerprint methods as well as the development of quantitative structure-activity relationship (QSAR) models for the well-known amines based on NNu values derived from DFT calculations. Utilizing explainable Shapley Additive Explanation plots, we were able to determine the five critical substructures that significantly impact the NNu values of amine. The aforementioned conclusion can be applied to produce and cultivate 4920 novel hypothetical amines with high NNu values. The QSAR models were employed to predict the NNu values of 259 well-known and 4920 hypothetical amines, resulting in the identification of five novel hypothetical amines with exceptional NNu values (>4.55 eV). The enhanced NNu values of these novel amines were validated by DFT calculations. One novel hypothetical amine, H1, exhibits an unprecedentedly high NNu value of 5.36 eV, surpassing the maximum value (5.35 eV) observed in well-established amines. Our research strategy efficiently accelerates the discovery of the high nucleophilicity of amines using ML predictions, as well as the DFT calculations.

Sustainable H2O2 production via solution plasma catalysis

Clean production of hydrogen peroxide (H2O2) with water, oxygen, and renewable energy is considered an important green synthesis route, offering a valuable substitute for the traditional anthraquinone method. Currently, renewable energy-driven production of H2O2 mostly relies on soluble additives, such as electrolytes and sacrificial agents, inevitably compromising the purity and sustainability of H2O2. Herein, we develop a solution plasma catalysis technique that eliminates the need for soluble additives, enabling eco-friendly production of concentrated H2O2 directly from water and O2. Screening over 40 catalysts demonstrates the superior catalytic performance of carbon nitride interacting with discharge plasma in water. High-throughput density functional theory calculations for 68 models, along with machine learning using 29 descriptors, identify cyano carbon nitride (CCN) as the most efficient catalyst. Solution plasma catalysis with the CCN achieves concentrated H2O2 of 20 mmol L-1, two orders of magnitude higher than photocatalysis by the same catalyst. Plasma diagnostics, isotope labeling, and COMSOL simulations collectively validate that the interplay of solution plasma and the CCN accounts for the significantly increased production of singlet oxygen and H2O2 thereafter. Our findings offer an efficient and sustainable pathway for H2O2 production, promising wide-ranging applications across the chemical industry, public health, and environmental remediation.

Computational Modeling and Machine Learning Approaches for Accelerated Materials Design

A substantial part of research in energy storage and conversion systems concerns the discovery and characterization of novel materials with improved performance, but most advancements are still generally attributed to costly and time-consuming trial-and-error experimentation. Computational chemistry methods combined with machine learning techniques offer paradigm shift in how materials are fundamentally understood and designed. Namely, high-throughput calculations combined with machine learning can help evaluate different properties of complex materials and efficiently screen millions of candidates to identify the ones with improved targeted properties, such as higher activity and selectivity and/or enhanced durability.In this talk we will first illustrate the use of density functional theory (DFT) in the design of fuel cell and electrolyzer ionomers. More specifically, we will discuss how simple descriptors such as DFT-calculated adsorption energies and adsorption modes of polymer fragments can be used to guide the rational design of ionomers for anion exchange membrane fuel cells and alkaline membrane water electrolyzers. Our design principle has led to the synthesis of new polymers, e.g., poly(fluorene) and polynorbornene-based ionomers, with weak adsorption on the electrocatalyst surface and improved hydrogen oxidation reaction activity. We will further introduce the computational workflow that integrates high-throughput DFT calculations with machine learning with a goal of designing new amine-based CO2 adsorbents, as well as platinum group metal-free electrocatalysts for conversion of CO2 to value added products. In our workflow we use descriptors together with DFT-calculated binding energetics to train artificial neural networks for activity prediction. Our trained machine learning models are then used to screen millions of candidate chemistries and identify the ones with optimal activity. Descriptor importance evaluation provides additional design principles based on the structure to property relationships for the synthesis of targeted materials for CO2 capture and electro-conversion.

Breaking the scaling relations of effective CO2 electrochemical reduction in diatomic catalysts by adjusting the flow direction of intermediate structures.

The electrocatalytic carbon dioxide reduction reaction (CO2RR) is a promising approach to achieving a sustainable carbon cycle. Recently, diatomic catalysts (DACs) have demonstrated advantages in the CO2RR due to their complex and flexible active sites. However, our understanding of how DACs break the scaling relationship remains insufficient. Here, we investigate the CO2RR of 465 kinds of graphene-based DACs (M1M2-N6@Gra) formed from 30 metal atoms through high-throughput density functional theory (DFT) calculations. We find that the intermediates *COOH, *CO, and *CHO have multiple adsorption states, with 11 structural flow directions from *CO to *CHO. Four of these structural flow directions have catalysts that can break the linear scale relationship. Based on the adsorption energy relationship between *COOH, *CHO and *CO, we propose the concepts of linear scaling, moderate breaking, and severe deviation regions, leading to the establishment of new descriptors that identify 14 catalysts with potential superior performance. Among them, ZnRu-N6@Gra and CrNi-N6@Gra can reduce CO2 to CH4 at a low limiting potential. We also discovered that DACs have independent bidirectional electron transfer channels during the adsorption and activation of CO2, which can significantly improve the flexibility and efficiency of regulating the electronic structure. Furthermore, through machine learning (ML) analysis, we identify electronegativity, atomic number, and d electron count as key determinants of catalyst stability. This work provides new insights into the understanding of the DAC catalytic mechanism, as well as the design and screening of catalysts.

Correlating Nitrate Adsorption with the Local Environments of FeCoNiCuZn High-Entropy Alloy Catalysts Using Machine Learning.

The electrocatalytic nitrate reduction to ammonia holds significant values for water remediations and energy applications, which quests for the development of highly effective catalysts with considerable stability and selectivity. Recently, high-entropy alloys (HEAs) are attracting growing attention for electrocatalytic processes. Nonetheless, studies of HEA-based nitrate reduction to ammonia are still at the early stage, and it remains unclear how the HEA compositions affect the adsorption and activation of the reaction intermediates. Herein, high-throughput density functional theory (DFT) calculations were integrated with machine learning to investigate the dependence of nitrate adsorption on the FeCoNiCuZn HEA structures. In particular, a total of 1268 different structures were sampled and constructed from the multidimensional configuration space, followed by the DFT calculations to investigate the Gibbs free energy of nitrate adsorption (i.e., ΔGNO3) on different surface microstructures. Four regression models were successfully developed, which can accurately predict ΔGNO3 using the HEA structures as the input features. Through the analysis of the feature importance, it was found that the active sites are crucial for nitrate adsorption; meanwhile, the local environments also play a considerable role. The dependence of the ΔGNO3 and adsorption geometries on the HEA compositions demonstrates that the compositional modulation of the HEA catalysts could be a promising avenue for facile adsorption and activation of reaction intermediates. Overall, this work will contribute to the probabilistic optimization of the HEA microstructures for enhanced electrochemical nitrate reduction.

High-Throughput Screening of All-d-Metal Heusler Alloys for Magnetocaloric Applications.

Due to their versatile composition and customizable properties, A2BC Heusler alloys have found applications in magnetic refrigeration, magnetic shape memory effects, permanent magnets, and spintronic devices. The discovery of all-d-metal Heusler alloys with improved mechanical properties compared to those containing main group elements presents an opportunity to engineer Heusler alloys for energy-related applications. Using high-throughput density-functional theory calculations, we screened magnetic all-d-metal Heusler compounds and identified 686 (meta)stable compounds. Our detailed analysis revealed that the inverse Heusler structure is preferred when the electronegativity difference between the A and B/C atoms is small, contrary to conventional Heusler alloys. Additionally, our calculations of Pugh ratios and Cauchy pressures demonstrated that ductile and metallic bonding are widespread in all-d-metal Heuslers, supporting their enhanced mechanical behavior. We identified 49 compounds with a double-well energy surface based on Bain path calculations and magnetic ground states, indicating their potential as candidates for magnetocaloric and shape memory applications. Furthermore, by calculating the free energies, we propose that 11 compounds exhibit structural phase transitions and suggest isostructural substitutions to enhance the magnetocaloric effect.

High-Throughput screening of metal nitrides for electrochemical nitrogen reduction

Metal nitrides have garnered considerable attention in the context of nitrogen reduction reactions (NRR) due to their rich lattice nitrogen and abundant nitrogen vacancies to activate N2. However, the identification of suitable metal nitrides with superior stability, activity, and selectivity (vs. hydrogen evolution reaction, HER) for electrochemical NRR under rigorous isotope labeling experiments remains a perplexing challenge in experiments. In this study, we implemented a systematic screening protocol to assess the electrochemical stability and efficiency of various metal nitrides for NRR by high-throughput density functional theory calculations, aiming to search out promising metal nitride catalysts. Through a set of predetermined criteria, a subset of 6 candidates emerges as the most promising metal nitrides for NRR from a pool of 668 metal nitrides sourced from the Materials Project Database, all of which catalyze NRR following the Mars-van Krevelen pathway. Importantly, based on the analysis of these candidates, the unique structural property (i.e., four(or three)-coordinate lattice nitrogen) and energy descriptor (i.e., the formation energy of the lattice nitrogen vacancy is around 2.1 eV) of potential metal nitrides for electrochemical NRR were extracted, which could be used as the direction to rationally design metal nitride catalyst. This research not only serves as a valuable guide for experimental investigations into potential metal nitrides for NRR, but also provides an efficient method for high-throughput screening of potential catalysts for other electrochemical reactions.

Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery.

The discovery of new crystalline inorganic compounds-novel compositions of matter within known structure types, or even compounds with completely new crystal structures-constitutes an important goal of solid-state and materials chemistry. Some fractions of new compounds can eventually lead to new structural and functional materials that enhance the efficiency of existing technologies or even enable completely new technologies. Materials researchers eagerly welcome new approaches to the discovery of new compounds, especially those that offer the promise of accelerated success. The recent report from a group of scientists at Google who employ a combination of existing data sets, high-throughput density functional theory calculations of structural stability, and the tools of artificial intelligence and machine learning (AI/ML) to propose new compounds is an exciting advance. We examine the claims of this work here, unfortunately finding scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility. While the methods adopted in this work appear to hold promise, there is clearly a great need to incorporate domain expertise in materials synthesis and crystallography.

High-throughput thermoelectric materials screening by deep convolutional neural network with fused orbital field matrix and composition descriptors

Thermoelectric materials harvest waste heat and convert it into reusable electricity. Thermoelectrics are also widely used in inverse ways such as refrigerators and cooling electronics. However, most popular and known thermoelectric materials to date were proposed and found by intuition, mostly through experiments. Unfortunately, it is extremely time and resource consuming to synthesize and measure the thermoelectric properties through trial-and-error experiments. Here, we develop a convolutional neural network (CNN) classification model that utilizes the fused orbital field matrix and composition descriptors to screen a large pool of materials to discover new thermoelectric candidates with power factor higher than 10 μW/cm K2. The model used our own data generated by high-throughput density functional theory calculations coupled with ab initio scattering and transport package to obtain electronic transport properties without assuming constant relaxation time of electrons, which ensures more reliable electronic transport properties calculations than previous studies. The classification model was also compared to some traditional machine learning algorithms such as gradient boosting and random forest. We deployed the classification model on 3465 cubic dynamically stable structures with non-zero bandgap screened from Open Quantum Materials Database. We identified many high-performance thermoelectric materials with ZT > 1 or close to 1 across a wide temperature range from 300 to 700 K and for both n- and p-type doping with different doping concentrations. Moreover, our feature importance and maximal information coefficient analysis demonstrates two previously unreported material descriptors, namely, mean melting temperature and low average deviation of electronegativity, that are strongly correlated with power factor and thus provide a new route for quickly screening potential thermoelectrics with high success rate. Our deep CNN model with fused orbital field matrix and composition descriptors is very promising for screening high power factor thermoelectrics from large-scale hypothetical structures.

High-throughput screening of N-doped curved graphene-loaded transition metal single-atom catalysts for nitrogen reduction reaction

Nitrogen reduction reaction (NRR) is necessary as an environmentally friendly and sustainable method for producing NH3 in the context of the energy crisis and increasing environmental concerns. Efficient catalysts play an important role in the NRR reaction. In this work, a high-curvature tetracoordinate transition metal (TM) nitrogen-doped graphene single-atom catalyst was designed and five candidate catalysts with excellent performances were selected from 406 models through a combination of high-throughput screening and density functional theory calculations. Using seven different coordination types MC4, MC3N1, MC2N2-1, MC2N2-2, MC2N2-3, MC1N3 and MN4, of which VN4 (side-on) with four N-atom coordination has the lowest limiting potential of 0.31 V and the competing Hydrogen Evolution Reaction (HER) was suppressed. This work provides guidance for the development of efficient NRR catalysts.

Realizing a high OER activity in new single-atom catalysts formed by introducing TMNx (x = 3 and 4) units into carbon nanotubes using high-throughput calculations.

Exploring highly efficient electrocatalysts for the oxygen evolution reaction (OER) is of great significance for hydrogen production through water splitting. By means of high-throughput density functional theory (DFT) calculations, we investigated the OER catalytic activity of a series of one-dimensional carbon nanotube (CNT)-based systems containing TMN4 or TMN3 functional units. Through the screening of 3d/4d/5d transition metals (TMs) from Group IVB to Group VIII, eight newly obtained TMNx@CNT (x = 3 and 4) systems were found to exhibit excellent OER activity, with very low overpotentials in the range 0.29-0.51 V, where the Co, Rh, Ir, Ti, Fe, and Ru atoms could be used as active sites. It was found that under the framework of TMN3@CNTs, the pre-adsorption of some species from water dissociation on the relevant TM sites (TM = Ti, Fe, and Ru) could lead to a high OER catalytic activity, which was different from the general situation where OER reactions directly occur on the clean surfaces of the remaining systems with Co/Rh/Ir metal centers. Moreover, the catalytic mechanisms were analyzed in detail. This work can be conducive to obtaining low-cost and high-performance OER single-atom electrocatalysts based on excellent CNT nanomaterials.

Discovery of highly efficient dual-atom catalysts for propane dehydrogenation assisted by machine learning.

Propane dehydrogenation (PDH) is a highly efficient approach for industrial production of propylene, and the dual-atom catalysts (DACs) provide new pathways in advancing atomic catalysis for PDH with dual active sites. In this work, we have developed an efficient strategy to identify promising DACs for PDH reaction by combining high-throughput density functional theory (DFT) calculations and the machine-learning (ML) technique. By choosing the γ-Al2O3(100) surface as the substrate to anchor dual metal atoms, 435 kinds of DACs have been considered to evaluate their PDH catalytic activity. Four ML algorithms are employed to predict the PDH activity and determine the relationship between the intrinsic characteristics of DACs and the catalytic activity. The promising catalysts of CuFe, CuCo and CoZn DACs are finally screened out, which are further validated by the whole kinetic reaction calculations, and the highly efficient performance of DACs is attributed to the synergistic effects and interactions between the paired active sites.

Development of machine learning interatomic potential for zinc

The HCP structure of zinc (Zn) exhibits an anomalous c/a ratio, a factor that traditional empirical potentials find challenging to reproduce accurately, thus impeding thorough investigations of its property and behavior via realistic atomistic simulations. The advent of machine learning interatomic potentials in recent years has ushered in new opportunities for the development of reliable Zn potentials. In this study, we construct a large set of atomic configurations composed of Zn atoms and calculate their energy, atomic forces, and stress tensor through high-throughput density functional theory calculations. Based on the calculated data, a moment tensor potential for Zn is developed, whose fidelity is then thoroughly examined by comprehensive comparison to existing potentials from the literature, density function theory calculations, and/or experimental measurements. It is found that the constructed potential can accurately reproduce the c/a ratio of HCP Zn, the relative stability of different crystal phases, the elastic properties, and defect properties of Zn. It can also describe reliably the structure, thermodynamic, and kinetic properties of Zn melt. This potential therefore offers a reliable tool for further exploration of the property and behavior of Zn at the atomic scale.

New insight into biodegradation mechanism of phenylurea herbicides by cytochrome P450 enzymes: Successive N-demethylation mechanism

Phenylurea herbicides (PUHs) present one of the most important herbicides, which have cause serious effects on ecological environment and humans. Nowadays enzyme strategy shows great advantages in degradation of PUHs. Here density functional theory (DFT), quantitative structure − activity relationship (QSAR) and quantum mechanics/molecular mechanics (QM/MM) approaches are used to investigate the degradation mechanism of PUHs catalyzed by P450 enzymes. Two successive N-demethylation pathways are identified and two hydrogen abstraction (H-abstraction) reaction pathways are identified as the rate-determining step through high-throughput DFT calculations. The Boltzmann-weighted average energy barrier of the second H-abstraction pathway (19.95 kcal/mol) is higher than that of the first H-abstraction pathway (16.80 kcal/mol). Two QSAR models are established to predict the energy barriers of the two H-abstraction pathways based on the quantum chemical descriptors and mordred molecular descriptors. The determination coefficient (R2) values of QSAR models are > 0.9, which reveal that the established QSAR models have great predictive capability. QM/MM calculations indicate that human P450 enzymes are more efficient in degradation of PUHs than crop and weed P450 enzymes. Correlations between energy barriers and key structural/charge parameters are revealed and key parameters that have influence on degradation efficiency of PUHs are identified. This study provides lateral insights into the biodegradation strategy and removal method of PUHs and valuable information for designing or engineering of highly efficient degradation enzymes and genetically modified crops.

A computational approach for mapping electrochemical activity of multi-principal element alloys

Multi principal element alloys (MPEAs) comprise an atypical class of metal alloys. MPEAs have been demonstrated to possess several exceptional properties, including, as most relevant to the present study a high corrosion resistance. In the context of MPEA design, the vast number of potential alloying elements and the staggering number of elemental combinations favours a computational alloy design approach. In order to computationally assess the prospective corrosion performance of MPEA, an approach was developed in this study. A density functional theory (DFT) – based Monte Carlo method was used for the development of MPEA ‘structure’; with the AlCrTiV alloy used as a model. High-throughput DFT calculations were performed to create training datasets for surface activity/selectivity towards different adsorbate species: O2-, Cl- and H+. Machine-learning (ML) with combined representation was then utilised to predict the adsorption and vacancy energies as descriptors for surface activity/selectivity. The capability of the combined computational methods of MC, DFT and ML, as a virtual electrochemical performance simulator for MPEAs was established and may be useful in exploring other MPEAs.

Quantitative prediction of corrosion resistance in ferrous alloys: A first-principles study of coupled doping of alloying elements

The corrosion resistance of ferrous alloys is greatly influenced by the presence of alloying elements, however, predicting the coupling effects of these elements remains a challenge. In the present paper, the effects of various alloying elements on the corrosion resistance of iron matrix material were investigated using high-throughput density functional theory (DFT) calculations. This work explores the dissolution behavior of metal and alloy surfaces and established correlations between calculated parameters and experimentally measurable values to predict polarization curves and simulate corrosion behavior. The results showed that doping with Cu, P, Ni, Al, Mn, Co, Si, Ti, and Zn on the Fe (110) surface can reduce the corrosion current density of the iron matrix. For Fe-Cu and Fe-Mn steels, coupling P-Ti in Fe-Cu steel and P-Si in Fe-Mn steel can significantly improve their corrosion resistance. The developed model can quantitatively predict material properties and efficiently screen compositions to design corrosion-resistant alloys.

Multiobjective Solid Electrolyte Design of Tetragonal and Cubic Inverse-Perovskites for All-Solid-State Lithium-Ion Batteries by High-Throughput Density Functional Theory Calculations and AI-Driven Methods

Multiobjective Solid Electrolyte Design of Tetragonal and Cubic Inverse-Perovskites for All-Solid-State Lithium-Ion Batteries by High-Throughput Density Functional Theory Calculations and AI-Driven Methods

High-throughput design of Co-based magnetic Heusler compounds

High-throughput (HTP) density functional theory (DFT) calculations are carried out on Co-based Heusler alloys, i.e., Co2YZ and X2CoZ (where X, Y, and Z range from lithium (Li) to bismuth (Bi) excluding inert gas and 4f rare-earth elements) in both cubic and tetragonal structures of the regular and inverse Heusler phases. After evaluating the thermodynamic stabilities based on the formation energy (ΔEf) and the distance to the convex hull (ΔEHD), we validate the HTP calculations with 65 experimentally known cases and further predict 158 novel compounds with ΔEHD smaller than 50 meV/atom, in which 117 compounds exhibit finite magnetization larger than 1 μB/f.u.. The novel Co-based Heuslers predicted by our HTP work also includes some all-d-metal compounds. In contrast to the conventional Heuslers with dominating pd hybridization, the all-d-metal Heuslers consisting of mainly dd hybridization cannot be properly interpreted by Burch’s rule. The Slater–Pauling rule, on the other hand, is found to well describe the relationship between the intrinsic magnetic properties, including the total spin moment and Curie temperature, and the number of valence electrons. Furthermore, magneto-crystalline anisotropy is calculated for stable tetragonal compounds, providing useful information for potential applications in the fields of permanent magnets and magnetic recording materials.

Accelerating the Discovery of Hybrid Perovskites with Targeted Band Gaps via Interpretable Machine Learning.

The band gap of hybrid organic-inorganic perovskites (HOIPs) is a key factor affecting the light absorption characteristics and thus the performance of perovskite solar cells (PSCs). However, band gap engineering, using experimental trial and error and high-throughput density functional theory calculations, is blind and costly. Therefore, it is critical to statistically identify the multiple factors influencing band gaps and to rationally design perovskites with targeted band gaps. This study combined feature engineering, the gradient-boosted regression tree (GBRT) algorithm, and the genetic algorithm-based symbolic regression (GASR) algorithm to develop an interpretable machine learning (ML) strategy for predicting the band gap of HOIPs accurately and quantitatively interpreting the factors affecting the band gap. Seven best physical features were selected to construct a GBRT model with a root-mean-square error of less than 0.060 eV, and the most important feature is the electronegativity difference between the B-site and the X-site (χB-X). Further, a mathematical formula (Eg = χB-X2 + 0.881χB-X) was constructed with GASR for a quantitative interpretation of the band gap influence patterns. According to the ML model, the HOIP MA0.23FA0.02Cs0.75Pb0.59Sn0.41Br0.24I2.76 was obtained, with a suitable band gap of 1.39 eV. Our proposed interpretable ML strategy provides an effective approach for developing HOIP structures with targeted band gaps, which can also be applied to other material fields.

Rational Modulation of Single Atom Coordination Microenvironments in a BCN Monolayer for Multifunctional Electrocatalysis.

Single-atom (SA) catalysts (SACs) have demonstrated outstanding catalytic performances toward plenty of relevant electrochemical reactions. Nevertheless, controlling the coordination microenvironment of catalytically active SAs to further enhance their catalytic oerformences has remained elusive up to now. Herein, a systematic investigation of 20 transition metal atoms that are coordinated with 20 different microenvironments in a boroncarbon-nitride monolayer (BCN) is conducted using high-throughput density functional theory calculations. The experimentally synthesized ternary BCN monolayer contains carbon, nitrogen, and boron atoms in its 2D network, thus providing a lot of new coordination environments than those of the current Cx Ny nanoplatforms. By exploring the structural/electrochemical stability, catalytic activity, selectivity, and electronic properties of 400 (20 × 20) TM-BCN moieties, it is discovered that specific SA coordination environments can achieve superior stability and selectivity for different electrocatalytic reactions. Moreover, a universal descriptor to accelerate the experimental process toward the synthesis of BCN-SACs is reported. These findings not only provide useful guidance for the synthesis of efficient multifunctional BCN-SACs but also will immediately benefit researchers by levering up their understanding of the mechanistic effects of SA coordination microenvironments on electrocatalytic reactions.