Pseudopotentials for high-throughput DFT calculations

High-throughput density functional theory (DFT) calculations are now commonly employed to screen materials for diverse applications and to populate databases of molecular properties. More recently, high-throughput DFT has been used to generate computational reaction networks for the analysis of reactive pathways. The automation of DFT calculations related to electrochemical systems has thus far been limited. Geometry optimization and SCF convergence are inherently more difficult when considering charged and open-shell molecules, coordinated metal ions, and solvated species, all of which are critically important for studies involving liquid-phase electrochemical environments such as those found in batteries. In addition, the chemical complexity of such species necessitates advanced levels of theory. We present a framework to automate the calculation of fully-optimized molecular geometries and molecular thermodynamic properties (enthalpy, entropy, and free energy) for molecules representing all the above challenges, including radical, charged, metal-coordinated, and solvated species. Our framework leverages the pymatgen,[1] fireworks,[2] custodian, and atomate[3] libraries to prepare, execute, and process electronic structure calculations using the Q-Chem DFT code.[4] Through benchmarking studies, we have identified appropriate methods for use in electrochemical environments, including an accurate density functional, basis set, and implicit solvent model. We have also implemented algorithms to correct common issues such as SCF convergence failures, geometric optimization to a non-minimal stationary point on the potential energy surface (a saddle point), and many more. In general, even when using an appropriate level of theory, 25% of calculations fail in some way without additional error correction. Our framework, using our suggested parameters and a suite of on-the-fly error handlers, converges to a minimum energy structure and calculates the molecular thermodynamic properties successfully in over 97% of cases. Individual calculations using our framework can be combined to perform complex workflows in a fully automated fashion. To date, we have applied this framework to over 25,000 unique molecules relevant to the formation of solid-electrolyte interphases in Li-ion and Mg-ion batteries.[1] S. P. Ong et al., Computational Materials Science, 68, 314–319 (2013).[2] A. Jain et al., Concurrency and Computation: Practice and Experience, 27(17), 5037-5059 (2015).[3] K. Mathew et al., Computational Materials Science, 139, 140-152 (2017).[4] Y. Shao et al., Molecular Physics, 113(2), 184-215 (2015). Figure 1

High-throughput identification of high activity and selectivity transition metal single-atom catalysts for nitrogen reduction

Understanding the intricate pathways of ethylene carbonate (EC) decomposition is pivotal for enhancing the stability and efficiency of electrolytes in various electrochemical applications. In this study, we delve into the decomposition mechanisms of EC in the presence of superoxide and peroxide species. The decomposition of EC-based electrolytes causes gas (carbon dioxide, carbon monoxide and hydrogen) evolution. Since this gas evolution is observed at potentials much lower than the oxidation potential of EC, it has been suggested that the mechanisms leading to decomposition are primarily chemical in nature (i.e., not related to the electrochemical oxidation of EC) and related to the reaction of reactive oxygen [singlet, superoxide, peroxide] with EC. [1,2,3]. Utilizing high-throughput Density Functional Theory (DFT) calculations, we meticulously explored the reaction networks of EC with superoxide and peroxide species across different cathode voltages. Our findings unveil compelling insights into the primary pathways dictating EC decomposition, which notably occur at potentials significantly lower than the oxidation potential of EC. Monte Carlo simulations were performed to understand the major products of the reaction network. Carbon dioxide is one of the major products in the reaction networks. Other major products include mono-carbonate, formaldehyde and organic products containing O-O in the fragments. We find that the products with O-O bond in the organic chain were also previously observed through mass spectroscopy experiments in [4]. Thus, using high-throughput data-driven approach, we are able to identify novel decomposition pathways of ethylene carbonate near the cathode.Reference[1] Freiberg, Anna TS, et al. “Singlet oxygen reactivity with carbonate solvents used for Li-ionbattery electrolytes.” The Journal of Physical Chemistry A 122.45 (2018): 8828-8839.[2] Wandt, Johannes, et al. “Singlet oxygen evolution from layered transition metal oxidecathode materials and its implications for lithium-ion batteries.” Materials Today 21.8 (2018):825-833.[3] Rinkel, Bernardine LD, et al. “Electrolyte oxidation pathways in lithium-ionbatteries.” Journal of the American Chemical Society 142.35 (2020): 15058-15074.[4] Kaufman, et al. “Surface Lithium Carbonate Influences Electrolyte Degradation via Reactive Oxygen Attack in Lithium-Excess Cathode Materials” Journal of the American Chemical Society (2021): 4170–4176

Hybrid organic/inorganic halide perovskites are considered to be a key material for high-end applications such as photovoltaic and light-emitting devices. Despite the phase stability and toxicity issues, the future potential of these materials is promising. A computational approach to discover novel perovskites based on density functional theory (DFT) calculations is booming since it is more favorable in terms of cost savings compared with the experimental synthesis approach. High-throughput DFT calculations associated with machine learning (ML) algorithms have recently attracted a great deal of attention in materials research. Rather than typical ML modeling and high-throughput DFT calculations, we suggest a direct discovery of novel perovskites using metaheuristic optimization algorithms in association with conventional lab-scale DFT calculations. Both an elitism-reinforced non-dominated sorting genetic algorithm (NSGA-II) and a reference point-involved NSGA-II (NSGA-III) were employed to nominate 25 novel perovskites (or their variants) that would be free from any toxic elements including Pb. The formation energy, band gap, and effective mass for these novel materials are all adequate for photovoltaic and light-emitting applications. While the ML-based prediction has an inverse prediction complication and even requires DFT calculation-based revalidation, the suggested strategy provides a process for direct discovery with no increase in computational cost.

Materials design based on density functional theory (DFT) calculations is an emergent field of great potential to accelerate the development and employment of novel materials. Magnetic materials play an essential role in green energy applications as they provide efficient ways of harvesting, converting, and utilizing energy. In this review, after a brief introduction to the major functionalities of magnetic materials, we demonstrated how the fundamental properties can be tackled via high-throughput DFT calculations, with a particular focus on the current challenges and feasible solutions. Successful case studies are summarized on several classes of magnetic materials, followed by bird-view perspectives.

Materials design based on density functional theory (DFT) calculations is an emergent field of great potential to accelerate the development and employment of novel materials. Magnetic materials play an essential role in green energy applications as they provide efficient ways of harvesting, converting, and utilizing energy. In this review, after a brief introduction to the major functionalities of magnetic materials, we demonstrated how the fundamental properties can be tackled via high-throughput DFT calculations, with a particular focus on the current challenges and feasible solutions. Successful case studies are summarized on several classes of magnetic materials, followed by bird-view perspectives.

Li-rich materials Li2MnO3-LiMO2 (M = Co, Ni, Mn) have received a great deal of research attention due to their attractive properties of high reversible capacity and energy density as lithium-ion battery (LIB) cathodes. Unfortunately, the commercialization of the materials has been hindered by numerous technical issues and challenges, notably the lack of clear understanding of the mechanism causing charge and discharge irreversibility. For understanding the mechanism, a lot of effort has been put into examining the irreversible deterioration of Li2MnO3, for instance in [1] by analyzing the effect of the lithium diffusion, the oxygen deficiency, and other factors on structural evolution and charge compensation. However, most previous works mainly focused on bulk cathode materials, while the surface region is also expected to remarkably affect the electrochemical activities of the materials. In this regard, the link between stable Li/vacancy configurations and Li concentration in the surface region is therefore essential for understanding the electrochemical properties and behavior of the surface region during charge and discharge. On the other hand, high-throughput computational materials design has emerged as a powerful and promising approach for the discovery and comprehensive analysis of materials [2]. Examples for LIB research include [3], where 515 stable lithiation reactions of some selected anode materials were enumerated based on a calculated energy data set of 291 compounds. Recently, the relationship between voltage and safety of LIBs was investigated using 1,400 cathode materials in an identical high-throughput fashion [4]. In this work, we exhaustively examine the relationship between electrochemically stable Li/vacancy configurations and their corresponding voltage in the surface region of Li2MnO3 in the solid-state Li2MnO3-LiMO cathode material through high-throughput computing. Figure 1 shows the slab model of Li2MnO3 (010) with the equivalent cell of Li16Mn8O24 used in this study. For each x in Li2−x MnO3, where 0.0 ≤ x ≤ 2.0 indicating that up to all of 16 Li atoms are subjected to removal, all possible placements of the remaining Li atoms in the lattice are considered, and the total energies of the resulting Li/vacancy structures are calculated, based on which the ground-state Li/vacancy structure is determined. Exploring all possible values of x for up to 16 Li atoms requires 216= 65,536 structures to be calculated and examined. With the aim of enabling the full exploration of such a large search space, we have developed our own software tool for high-throughput density functional theory (DFT) calculations that can effectively and reliably handle concurrent task executions, task and data management, and parameter optimization on a couple of different computing platforms. The calculations are performed by our tool utilizing the OpenMX code [5] as the DFT engine, with the spin-polarized GGA+U scheme having the Hubbard U value of Mn set at 5 eV to account for the strong correlation effect, the PBE functional, an energy cutoff of 300 Ry, and a k-point mesh of 3x3x1. Figure 2 presents several preliminary results showing the voltage profile with respect to x in the Li2−x MnO3 (010) surface region. As can be seen from the figure, although the voltage of the Li2−x MnO3 surface region tends to increase from about 4 V to 5 V with an increase in x where 0.0 ≤ x ≤ 0.5, it converges at around 5 V where 0.5 ≤ x ≤ 2.0. This voltage difference is found to be quite considerable in comparison with that of about 0.05 V in [1], which also observed a similar trend of voltage gain with an increase in x in the bulk Li2-x MnO3 (0.0 ≤ x ≤ 1.0). As a result, the link between voltage and x in the surface region is far more obvious than that in the bulk when the depth of charge is shallow. We will thoroughly analyze and report our latest findings based on a complete coverage of x in the presentation. Acknowledgement The calculations in this work were partly performed using the K computer at the RIKEN Advanced Institute for Computational Science.

Convergence and pitfalls of density functional perturbation theory phonons calculations from a high-throughput perspective

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.

Inspired by the synthesis of XB3C3 (X = Sr, La) compounds in the bipartite sodalite clathrate structure, density functional theory (DFT) calculations are performed on members of this family containing up to two different metal atoms. A DFT-chemical pressure analysis on systems with X = Mg, Ca, Sr, Ba reveals that the size of the metal cation, which can be tuned to stabilize the B-C framework, is key for their ambient-pressure dynamic stability. High-throughput density functional theory calculations on 105 Pm3̅ symmetry XYB6C6 binary-guest compounds (where X, Y are electropositive metal atoms) find 22 that are dynamically stable at 1 atm, expanding the number of potentially synthesizable phases by 19 (18 metals and 1 insulator). The density of states at the Fermi level and superconducting critical temperature, Tc, can be tuned by changing the average oxidation state of the metal atoms, with Tc being highest for an average valence of +1.5. KPbB6C6, with an ambient-pressure Eliashberg Tc of 88 K, is predicted to possess the highest Tc among the studied Pm3̅n XB3C3 or Pm3̅ XYB6C6 phases, and calculations suggest it may be synthesized using high-pressure high-temperature techniques and then quenched to ambient conditions.

Driven by the big data science, material informatics has attracted enormous research interests recently along with many recognized achievements. To acquire knowledge of materials by previous experience, both feature descriptors and databases are essential for training machine learning (ML) models with high accuracy. In this regard, the electronic charge density ρ(r), which in principle determines the properties of materials at their ground state, can be considered as one of the most appropriate descriptors. However, the systematic electronic charge density ρ(r) database of inorganic materials is still in its infancy due to the difficulties in collecting raw data in experiment and the expensive first-principles based computational cost in theory. Herein, a real space electronic charge density ρ(r) database of 17,418 cubic inorganic materials is constructed by performing high-throughput density functional theory calculations. The displayed ρ(r) patterns show good agreements with those reported in previous studies, which validates our computations. Further statistical analysis reveals that it possesses abundant and diverse data, which could accelerate ρ(r) related machine learning studies. Moreover, the electronic charge density database will also assists chemical bonding identifications and promotes new crystal discovery in experiments.

Looking for new thermoelectric materials among TMX intermetallics using high-throughput calculations

Insights into the ultra-high temperature solid solutions Hf-Ta-C-N quaternary system using high-throughput calculation

Solid electrolytes (SEs) are crucial materials to realize highly safe and practical all-solid-state Li+-ion batteries. Here, we performed a large-scale computational SE screening on a chemical space of >10 000 Li-rich inverse-perovskite (ip) compounds with tetragonal and cubic structures by high-throughput density functional theory (DFT) and AI-driven methods. A total of 1413 novel candidate compounds were predicted to be synthesizable based on thermodynamic decomposition energy (Ed) and machine-learned experimental synthesis likelihood (Ls). These compounds were further screened using a Pareto-front approximation set of a multiobjective Bayesian optimization tasks for k = 3 DFT-calculated SE properties (fk, with k = 1, 2, and 3): (i) electrochemical window from electronic band gap energy (f1: Eg), (ii) chemical stability by reaction with moisture (f2: Eh), and (iii) 400 K bulk Li+-ion conductivity (f3: Λ). As a result, the compound list was reduced down to 24 candidate ip SEs, and examples include Cm Li8O2Cl3Br (Ed = 0, Ls > 0.5, Eg = 4.74 eV, Eh = −33.22 kJ/mol, and Λ = 9.0 × 10–4 S/cm), Amm2 Li8OSCl4 (Ed = 0.070 eV/atom, Ls > 0.5, Eg = 4.14 eV, Eh = −40.70 kJ/mol, and Λ = 9.2 × 10–2 S/cm), and Cmcm Li12O3SeClBr3 (Ed = 0.097 eV/atom, Ls > 0.5, Eg = 3.36 eV, Eh = −86.88 kJ/mol, and Λ = 7.8 × 10–1 S/cm). Possible solid-state synthesis routes for the screened SE candidates were also explored using thermodynamic phase competition analysis and classical nucleation theory reaction barrier. Aside from providing a well-informed list of potentially novel ip-type SEs, our work also reports on an effective calculation methodology for tiered large-scale material screening which, at the same time, incorporates “small data” learning on target property datasets that are computationally expensive to obtain. The generated datasets are expected as well to be of great utility for future data-driven material design efforts.

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.