Search

Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales. Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales, but these force fields require substantial effort to construct and are highly specific to given chemical composition and application. The extreme flexibility of machine learning models promises to yield reactive force fields that provide a more general description of chemical bonding. However, a significant limitation of machine learning models is the use of element-specific features, leading to models that scale poorly with the number of elements. This work introduces the Gaussian multi-pole (GMP) featurization scheme that utilizes physically-relevant multi-pole expansions of the electron density around atoms to yield feature vectors that interpolate between element types and have a fixed dimension regardless of the number of elements present. We combine GMP with neural networks to directly compare it to the widely-used Behler-Parinello symmetry functions for the MD17 dataset, revealing that it exhibits improved accuracy and computational efficiency. Further, we demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements. Finally, we test GMP-based models for the Open Catalysis Project (OCP) dataset, revealing comparable performance and improved learning rates when compared to graph convolutional deep learning models. The results indicate that this featurization scheme fills a critical gap in the construction of efficient and transferable reactive force fields.

The study presents an ab-initio based framework for the automated construction of microkinetic mechanisms considering correlated uncertainties in all energetic parameters and estimation routines. Two thousand unique microkinetic models were generated within the uncertainty space of the BEEF-vdW functional for the conversion of exhaust gas emissions from stoichiometric gasoline combustion engines over Pt(111) and compared to experiments through multiscale modeling. The ensemble of simulations stresses the importance of considering uncertainties. Within this set of first-principles-based models, it is possible to identify a microkinetic mechanism that agrees with experimental data. This mechanism can be traced back to a single exchange-correlation functional, and it suggests that Pt(111) could be the active site for the oxidation of light hydrocarbons. The study provides a universal framework for the automated construction of reaction mechanisms with correlated uncertainty quantification, enabling a DFT-constrained microkinetic model optimization for other heterogeneously catalyzed systems.

The performance of Brønsted- and Lewis-acidic La, Nb, and Zr phosphates (LaPO, NbPO, and ZrPO) during the aqueous phase conversion of dihydroxyacetone (DHA) to lactic acid (LA) is investigated using a fixed-bed reactor. Mass-transfer phenomena are thoroughly investigated, and the masstransfer coefficient is deconvoluted from the intrinsic kinetic constant for each catalyst to enable the quantitative assessment of both. NbPO is found to be masstransfer-limited. Despite this limitation, NbPO shows the highest yield of LA at 36%. The reaction over ZrPO is not transport-limited, allowing for a rigorous analysis of intrinsic kinetics. This analysis shows that the conversion of DHA into pyruvaldehyde (PVA) follows a second-order reaction mechanism via a dimeric intermediate, which consolidates previous reports in the literature. Additionally, a correlation between LA production and the carbon missing from the carbon balance (carbon loss) is observed. Finally, NbPO and ZrPO show stable performance up to 10 h on stream at 150 °C. After 15 h of reaction, the PVA yield increases at the expense of LA with NbPO. This is ascribed to the deactivation of the active sites necessary to produce LA, which are different from the sites that produce PVA.This hypothesis is supported by the characterization of the spent catalyst with 13C magic-angle spinning nuclear magnetic resonance and attenuated total reflectance infrared spectroscopy.

Ab initio pseudopotentials are a linchpin of modern molecular and condensed matter electronic structure calculations. In this work, we employ multi-objective optimization to maximize pseudopotential softness while maintaining high accuracy and transferability. To accomplish this, we develop a formulation in which softness and accuracy are simultaneously maximized, with accuracy determined by the ability to reproduce all-electron energy differences between Bravais lattice structures, whereupon the resulting Pareto frontier is scanned for the softest pseudopotential that provides the desired accuracy in established transferability tests. We employ an evolutionary algorithm to solve the multi-objective optimization problem and apply it to generate a comprehensive table of optimized norm-conserving Vanderbilt (ONCV) pseudopotentials (https://github.com/SPARC-X/SPMS-psps). We show that the resulting table is softer than existing tables of comparable accuracy, while more accurate than tables of comparable softness. The potentials thus afford the possibility to speed up calculations in a broad range of applications areas while maintaining high accuracy., 13 pages, 4 figures

Energy-related descriptors in machine learning are a promising strategy to predict adsorption properties of metal–organic frameworks (MOFs) in the low-pressure regime. Interactions between hosts and guests in these systems are typically expressed as a sum of dispersion and electrostatic potentials. The energy landscape of dispersion potentials plays a crucial role in defining Henry’s constants for simple probe molecules in MOFs. To incorporate more information about this energy landscape, we introduce the Gaussian-approximated Lennard-Jones (GALJ) potential, which fits pairwise Lennard-Jones potentials with multiple Gaussians by varying their heights and widths. The GALJ approach is capable of replicating information that can be obtained from the original LJ potentials and enables efficient development of Gaussian integral (GI) descriptors that account for spatial correlations in the dispersion energy environment. GI descriptors would be computationally inconvenient to compute using the usual direct evaluation of the dispersion potential energy surface. We show that these new GI descriptors lead to improvement in ML predictions of Henry’s constants for a diverse set of adsorbates in MOFs compared to previous approaches to this task.

Environmentally friendly and energy-efficient ways to produce ammonia are essential to meet global food demands. Here, a new approach for ammonia production at nominally ambient conditions is intro...

Synthesis of ammonia through photo- and electrocatalysis is a rapidly growing field. Titania-based catalysts are widely reported for photocatalytic ammonia synthesis and have also been suggested as electrocatalysts. The addition of transition-metal dopants is one strategy for improving the performance of titania-based catalysts. In this work, we screen d-block transition-metal dopants for surface site stability and evaluate trends in their performance as the active site for the reduction of nitrogen to ammonia on TiO$_2$. We find a linear relationship between the d-band center and formation energy of the dopant site, while the binding energies of N$_2$, N$_2$H, and NH$_2$ all are strongly correlated with the cohesive energies of the dopant metals. The activity of the metal-doped systems shows a volcano type relationship with the NH$_2$ and N$_2$H energies as descriptors. Some metals such as Co, Mo, and V are predicted to slightly improve photo- and electrocatalytic performance, but most metals inhibit the ammonia synthesis reaction. The results provide insight into the role of transition-metal dopants for promoting ammonia synthesis, and the trends are based on unexpected electronic structure factors that may have broader implications for single-atom catalysis and doped oxides.

Understanding surface reactions of biomass-derived oxygenates on metal oxides is important for designing catalysts for valorization of biomass. This work elucidated the effect of different pretreat...

Summary Using solar energy to convert triple bonded molecular dinitrogen from the air into fixed nitrogen products that act as nutrients for plants presents an opportunity to develop “solar fertilizers.” The approach has much in common with solar fuels and chemicals but also has some unique advantages and challenges. The possibility of producing nitrogen fertilizers at a country’s regional level may be able to effectively compete with existing technology by reducing the per unit cost of N nutrient production, removing or reducing transportation costs within or across international borders, and integrating with existing infrastructure. Furthermore, solar fertilizer technologies can reduce the energy and carbon footprint currently associated with the Haber-Bosch process for ammonia synthesis. Deploying solar fertilizer technology in developing countries can also improve access to fertilizers for farmers in remote regions and help achieve the United Nations Sustainable Development Goal 2 of ending hunger and ensuring access to safe, nutritious, and sufficient food for all people, in particular the poor. However, there are also substantial challenges that must be overcome in identifying active catalytic materials and effectively integrating solar fertilizer processes with agricultural infrastructure. This paper outlines the agronomic considerations that drive the development of decentralized solar fertilizer production and explores their implications on fertilizer prices with an emphasis on the developing world. The work also provides an overview of the technical strategies that may enable photo(electro)chemical fertilizer production processes, including the use of fertigation or production of enhanced biochar, and identifies some target metrics and testing considerations to promote efficient development of new fertilizer materials and processes.

The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but typically do not incorporate population-level heterogeneity in infection susceptibility. Here we combine a generalized analytical framework of contagion with computational models of epidemic dynamics to show that variation strongly influences the rate of infection, while the infection process simultaneously sculpts the susceptibility distribution. These joint dynamics influence the force of infection and are, in turn, influenced by the shape of the initial variability. We find that certain susceptibility distributions (the exponential and the gamma) are unchanged through the course of the outbreak, and lead naturally to power-law behavior in the force of infection; other distributions are often sculpted towards these “eigen-distributions” through the process of contagion. The power-law behavior fundamentally alters predictions of the long-term infection rate, and suggests that first-order epidemic models that are parameterized in the exponential-like phase may systematically and significantly over-estimate the final severity of the outbreak. In summary, our study suggests the need to examine the shape of susceptibility in natural populations as part of efforts to improve prediction models and to prioritize interventions that leverage heterogeneity to mitigate against spread.

In this work, we employ hybrid and generalized gradient approximation (GGA) level density functional theory (DFT) calculations to investigate the convergence of surface properties and band structure of rutile titania (TiO$_2$) nanoparticles with particle size. The surface energies and band structures are calculated for cuboidal particles with minimum dimension ranging from 3.7 \r{A} (24 atoms) to 10.3 \r{A} (384 atoms) using a highly-parallel real-space DFT code to enable hybrid level DFT calculations of larger nanoparticles than are typically practical. We deconvolute the geometric and electronic finite size effects in surface energy, and evaluate the influence of defects on band structure and density of states (DOS). The electronic finite size effects in surface energy vanish when the minimum length scale of the nanoparticles becomes greater than 10 \r{A}. We show that this length scale is consistent with a computationally efficient numerical analysis of the characteristic length scale of electronic interactions. The surface energy of nanoparticles having minimum dimension beyond this characteristic length can be approximated using slab calculations that account for the geometric defects. In contrast, the finite size effects on the band structure is highly dependent on the shape and size of these particles. The DOS for cuboidal particles and more realistic particles constructed using the Wulff algorithm reveal that defect states within the bandgap play a key role in determining the band structure of nanoparticles and the bandgap does not converge to the bulk limit for the particle sizes investigated.

The industrial Haber–Bosch process to produce ammonia (NH3) from dinitrogen (N2) is crucial for modern society. However, N2 activation is inherently challenging and the Haber–Bosch process has significant drawbacks, as it is highly energy intensive, is not sustainable owing to substantial CO2 emissions primarily from the generation of H2 and requires large, centralized facilities. New strategies of sustainable N2 activation, such as low-temperature thermochemical catalysis and (photo)electrocatalysis, have been pursued, but progress has been hindered by the lack of rigour and reproducibility in the collection and analysis of results. In this Primer, we provide a holistic step by step protocol, applicable to all nitrogen-transformation reactions, focused on verifying genuine N2 activation by accounting for all contamination sources. We compare state-of-the-art results from different catalytic reactions following the protocol’s framework, and discuss necessary reporting metrics and ways to interpret both experimental and density functional theory results. This Primer covers various common pitfalls in the field, best practices to improve reproducibility and cost-efficient methods to carry out rigorous experimentation. The future of nitrogen catalysis will require an increase in rigorous experimentation and standardization to prevent false positives from appearing in the literature, which can enable advancing towards practical technologies for the activation of N2. This Primer highlights the range of new strategies for sustainable N2 activation and the step by step protocol necessary for evaluating genuine activity. The required metrics and how to interpret data alongside the best practices to improve reproducibility and enable the development of practical technologies are discussed.

An open-source, Python-based Temporal Analysis of Products (TAP) reactor simulation and processing program is introduced. TAPsolver utilizes algorithmic differentiation for the calculation of highly accurate derivatives, which are used to perform sensitivity analyses and PDE-constrained optimization. The tool supports constraints to ensure thermodynamic consistency, which can lead to more accurate descriptions of microkinetic models. The mathematical and structural details of TAPsolver are outlined, as well as validation of the forward and inverse problems against well-defined prototype problems. Benchmarks of the code are presented, and a case study for extracting kinetic parameters that do not violate thermodynamics from experimental TAP measurements of CO oxidation on supported platinum particles is presented. TAPsolver will act as a foundation for future development and dissemination of TAP data processing techniques.

Zeolites are nanoporous aluminosilicates widely used in catalysis and separations applications. Though generally formed as 3D crystals, new synthesis techniques have given access to 2D zeolite nanosheets with small diffusion path lengths and accelerated molecular diffusion. Since most previous research has focused on bulk zeolite crystals, there is little understanding of the surface adsorption and diffusion mechanisms likely involved at such length scales and their contributions to the permeability and selectivity of different species. To enable the systematic examination of such surface properties, we constructed a database of more than 800,000 computation-ready 2D zeolite nanosheets from the full range of known zeolite structures in the IZA database of zeolite structure types. The nanosheet surfaces cover a wide range of orientations and were created via the principle of minimizing the number of bonds broken during the termination of a unit cell. The database consists of two sets of nanosheets: one set...

Photo-catalytic fixation of nitrogen by titania catalysts at ambient conditions has been reported for decades, yet the active site capable of adsorbing an inert N2 molecule at ambient pressure and the mechanism of dissociating the strong dinitrogen triple bond at room temperature remain unknown. In this work in situ near-ambient-pressure X-ray photo-electron spectroscopy and density functional theory calculations are used to probe the active state of the rutile (110) surface. The experimental results indicate that photon-driven interaction of N2 and TiO2 is observed only if adventitious surface carbon is present, and computational results show a remarkably strong interaction between N2 and carbon substitution (C*) sites that act as surface-bound carbon radicals. A carbon-assisted nitrogen reduction mechanism is proposed and shown to be thermodynamically feasible. The findings provide a molecular-scale explanation for the long-standing mystery of photo-catalytic nitrogen fixation on titania. The results suggest that controlling and characterizing carbon-based active sites may provide a route to engineering more efficient photo(electro)-catalysts and improving experimental reproducibility.

Methanol is a major fuel and chemical feedstock currently produced from syngas, a CO/CO2/H2 mixture. Herein we identify formate binding strength as a key parameter limiting the activity and stability of known catalysts for methanol synthesis in the presence of CO2. We present a molybdenum phosphide catalyst for CO and CO2 reduction to methanol, which through a weaker interaction with formate, can improve the activity and stability of methanol synthesis catalysts in a wide range of CO/CO2/H2 feeds.

Catalysis informatics is a distinct subfield that lies at the intersection of cheminformatics and materials informatics but with distinctive challenges arising from the dynamic, surface-sensitive, and multiscale nature of heterogeneous catalysis. The ideas behind catalysis informatics can be traced back decades, but the field is only recently emerging due to advances in data infrastructure, statistics, machine learning, and computational methods. In this work, we review the field from early works on expert systems and knowledge engines to more recent approaches utilizing machine-learning and uncertainty quantification. The data–information–knowledge hierarchy is introduced and used to classify various developments. The chemical master equation and microkinetic models are proposed as a quantitative representation of catalysis knowledge, which can be used to generate explanative and predictive hypotheses for the understanding and discovery of catalytic materials. We discuss future prospects for the field, i...