Formamide reaction network in gas phase and solution via a unified theoretical approach: Toward a reconciliation of different prebiotic scenarios

The structure of water-ammonia mixtures from classical and ab initio molecular dynamics

The structure of aqueous ammonia solutions is investigated through classical molecular dynamics (MD) and ab initio molecular dynamics (AIMD) simulations. We have preliminarily compared three well-known classical force fields for liquid water (SPC, SPC/E, and TIP4P) in order to identify the most accurate one in reproducing AIMD results obtained at the Generalized Gradient Approximation (GGA) and meta-GGA levels of theory. Liquid ammonia has been simulated by implementing an optimized force field recently developed by Chettiyankandy et al. [Fluid Phase Equilib. 511, 112507 (2020)]. Analysis of the radial distribution functions for different ammonia concentrations reveals that the three water force fields provide comparable estimates of the mixture structure, with the SPC/E performing slightly better. Although a fairly good agreement between MD and AIMD is observed for conditions close to the equimolarity, at lower ammonia concentrations, important discrepancies arise, with classical force fields underestimating the number and strength of H-bonds between water molecules and between water and ammonia moieties. Here, we prove that these drawbacks are rooted in a poor sampling of the configurational space spanned by the hydrogen atoms lying in the H-bonds of H2O⋯H2O and, more critically, H2O⋯NH3 neighbors due to the lack of polarization and charge transfer terms. This way, non-polarizable classical force fields underestimate the proton affinity of the nitrogen atom of ammonia in aqueous solutions, which plays a key role under realistic dilute ammonia conditions. Our results witness the need for developing more suited polarizable models that are able to take into account these effects properly.

A New Route to the Prebiotic Synthesis of Glycine via Ab Initio-Based Machine Learning Calculations

In this work, we study the synthesis of glycine, the simplest amino acid, using ab initio molecular dynamics and enhanced sampling techniques to explore and quantify novel potential pathways. Our protocol integrates state-of-the-art machine learning approaches, allowing us to sample relevant chemical spaces more efficiently. We discover a novel "oxyglycolate path", distinct from the "standard" Strecker mechanism, identify new intermediates, and provide a full thermodynamic characterization of all reaction steps. This alternative pathway aligns better with meteoritic and experimental observations, paving the way for further investigations. Integrating quantum accuracy and machine learning in prebiotic chemistry represents a methodological milestone advancing the exploration of life's prebiotic origins.

Reaction mechanisms for methyl isocyanate (CH3NCO) gas-phase degradation

Methyl isocyanate (MIC) is a toxic chemical found in many commercial, industrial, and agricultural processes, and was the primary chemical involved in the Bhopal, India disaster of 1984. The atmospheric environmental chemical reactivity of MIC is relatively unknown with only proposed reaction channels, mainly involving OH-initiated reactions. The gas-phase degradation reaction pathways of MIC and its primary product, formyl isocyanate (FIC), were investigated with quantum mechanical (QM) calculations to assess the fate of the toxic chemical and its primary transformation products. Transition state energy barriers and reaction energetics were evaluated for thermolysis/pyrolysis-like reactions and bimolecular reactions initiated by relevant radicals (•OH and Cl•) to evaluate the potential energy surfaces and identify the primary reaction pathways and products. Thermolysis/pyrolysis of MIC requires high energy to initiate N-CH3 and C-H bond dissociation and is unlikely to dissociate except under extreme conditions. Bimolecular radical addition and H-abstraction reaction pathways are deemed the most kinetically and thermodynamically favorable mechanisms. The primary transformation products of MIC were identified as FIC, methylcarbamic acid, isocyanic acid (isocyanate radical), and carbon dioxide. The results of this work inform the gas-phase reaction channels of MIC and FIC reactivity and identify transformation products under various reaction conditions.

Water-Catalyzed Formation of Reactive Oxygen Species from NO2 on a Weakly Hydrated Calcite Surface

The interfaces of weakly hydrated mineral substrates have been shown to serve as catalytic sites for chemical reactions that may not be accessible in the gas phase or under bulk conditions. Currently known mechanisms for the formation of reactive oxygen species (ROS) from nitrogen dioxide (NO2) involve NO2 dimerization. Here, we report the formation of the ROS HONO via a mechanism involving simple adsorption of a single NO2 molecule on a weakly hydrated calcite substrate. First-principles molecular dynamics simulations coupled with enhanced sampling techniques show how an adsorbed water sublayer can enhance NO2 adsorption on calcite compared to adsorption on a bare dry substrate. On the weakly hydrated calcite surface, an interfacial electric field facilitates proton extraction from water, thus allowing HONO formation from a single adsorbed NO2, i.e., without the need for the formation of a NO2 dimer precomplex. HONO formation on calcite is kinetically more favorable than that in the gas phase, with a reaction barrier of 14 kcal/mol on the weakly hydrated calcite surface compared to 27 kcal/mol in the gas phase. Further photocatalysed HONO production by visible light and HONO dissociation are hampered on calcite, unlike the process on silica. NO2 is a significant anthropogenic pollutant, and understanding its chemistry is crucial for explaining the high ROS levels and haze formation in polluted areas or prebiotic ROS generation. These findings emphasize how mineral substrates under water-restricted hydration conditions can trigger chemical pathways that are unexpected in the gas phase or under bulk conditions.

Data-Driven Path Collective Variables

Identifying optimal collective variables to model transformations using atomic-scale simulations is a long-standing challenge. We propose a new method for the generation, optimization, and comparison of collective variables that can be thought of as a data-driven generalization of the path collective variable concept. It consists of a kernel ridge regression of the committor probability, which encodes a transformation's progress. The resulting collective variable is one-dimensional, interpretable, and differentiable, making it appropriate for enhanced sampling simulations requiring biasing. We demonstrate the validity of the method on two different applications: a precipitation model and the association of Li+ and F- in water. For the former, we show that global descriptors such as the permutation invariant vector allow reaching an accuracy far from the one achieved via simpler, more intuitive variables. For the latter, we show that information correlated with the transformation mechanism is contained in the first solvation shell only and that inertial effects prevent the derivation of optimal collective variables from the atomic positions only.

Revealing the Unique Role of Water in the Formation of Benzothiazoles: an Experimental and Computational Study

We present here a joint experimental and computational study on the formation of benzothiazoles. Our investigation reveals a green protocol for accessing benzothiazoles from acyl chlorides using either water alongside a reducing agent as the reaction medium or in combination with stoichiometric amounts of a weak acid, instead of the harsh conditions and catalysts previously reported. Specifically, we show that a protic solvent, particularly water, enables the formation of 2-substituted benzothiazoles from N-acyl 1,2-aminothiophenols already at room temperature, without the need for strong acids or metal catalysts. DFT Molecular Dynamics simulations coupled with advanced enhanced sampling techniques provide a clear understanding of the catalytic role of water. We demonstrate how bulk water - due to its extended network of hydrogen bonds and an efficient Grotthuss mechanism - provides a reaction path that strongly reduces the reaction barriers compared to aprotic environments, namely more than 80 kJ/mol for the first reaction step and 250 kJ/mol for the second. Finally, we discuss the influence of different aliphatic and aromatic substituents with varying electronic properties on chemical reactivity. Besides providing in-depth mechanistic insights, we believe that our findings pave the way for a greener route toward an important class of bioactive molecules.

Uptake and reactivity of NO2 on the hydroxylated silica surface: A source of reactive oxygen species

We report state-of-the-art first-principles molecular dynamics results on the heterogeneous chemical uptake of NO2, a major anthropogenic pollutant, on the dry and wet hydroxylated surface of α-quartz, which is a significant component of silica-based catalysts and atmospheric dust aerosols. Our investigation spotlights an unexpected chemical pathway by which NO2 (i) can be adsorbed as HONO by deprotonation of interfacial silanols (i.e., -Si-OH group) on silica, (ii) can be barrierless converted to nitric acid, and (iii) can finally dissociated to surface bounded NO and hydroxyl gas phase radicals. This chemical pathway does not invoke any previously experimentally postulated NO2 dimerization, dimerization that is less likely to occur at low NO2 concentrations. Moreover, water significantly catalyzes the HONO formation and the dissociation of nitric acid into surface-bounded NO and OH radicals, while visible light adsorption can further promote these chemical transformations. This work highlights how water-restricted solvation regimes on common mineral substrates are likely to be a source of reactive oxygen species, and it offers a theoretical framework for further and desirable experimental efforts, aiming to better constrain trace gases/mineral interactions at different relative humidity conditions.

Ambient-Temperature, Metal-Free, CDI-Mediated Ex-Situ Conversion of Acids to Amides: A Useful Late-Stage Strategy

An efficient ex-situ method for the amidation of carboxylic acids mediated by CDI has been disclosed herewith. This metal-free strategy is performed at ambient temperature and can be applied effectively for late-stage modification of amino acids and APIs.

The protometabolic nature of prebiotic chemistry

The field of prebiotic chemistry has been dedicated over decades to finding abiotic routes towards the molecular components of life. There is nowadays a handful of prebiotically plausible scenarios that enable the laboratory synthesis of most amino acids, fatty acids, simple sugars, nucleotides and core metabolites of extant living organisms. The major bottleneck then seems to be the self-organization of those building blocks into systems that can self-sustain. The purpose of this tutorial review is having a close look, guided by experimental research, into the main synthetic pathways of prebiotic chemistry, suggesting how they could be wired through common intermediates and catalytic cycles, as well as how recursively changing conditions could help them engage in self-organized and dissipative networks/assemblies (i.e., systems that consume chemical or physical energy from their environment to maintain their internal organization in a dynamic steady state out of equilibrium). In the article we also pay attention to the implications of this view for the emergence of homochirality. The revealed connectivity between those prebiotic routes should constitute the basis for a robust research program towards the bottom-up implementation of protometabolic systems, taken as a central part of the origins-of-life problem. In addition, this approach should foster further exploration of control mechanisms to tame the combinatorial explosion that typically occurs in mixtures of various reactive precursors, thus regulating the functional integration of their respective chemistries into self-sustaining protocellular assemblies.

Direct Synthesis of Formamide from CO2 and H2O with Nickel-Iron Nitride Heterostructures under Mild Hydrothermal Conditions

Formamide can serve as a key building block for the synthesis of organic molecules relevant to premetabolic processes. Natural pathways for its synthesis from CO2 under early earth conditions are lacking. Here, we report the thermocatalytic conversion of CO2 and H2O to formate and formamide over Ni-Fe nitride heterostructures in the absence of synthetic H2 and N2 under mild hydrothermal conditions. While water molecules act as both a solvent and hydrogen source, metal nitrides serve as nitrogen sources to produce formamide in the temperature range of 25-100 °C under 5-50 bar. Longer reaction times promote the C-C bond coupling and formation of acetate and acetamide as additional products. Besides liquid products, methane and ethane are also produced as gas-phase products. Postreaction characterization of Ni-Fe nitride particles reveals structural alteration and provides insights into the potential reaction mechanism. The findings indicate that gaseous CO2 can serve as a carbon source for the formation of C-N bonds in formamide and acetamide over the Ni-Fe nitride heterostructure under simulated hydrothermal vent conditions.

The Reactivity-Enhancing Role of Water Clusters in Ammonia Aqueous Solutions

Among the many prototypical acid-base systems, ammonia aqueous solutions hold a privileged place, owing to their omnipresence in various planets and their universal solvent character. Although the theoretical optimal water-ammonia molar ratio to form NH4+ and OH- ion pairs is 50:50, our ab initio molecular dynamics simulations show that the tendency of forming these ionic species is inversely (directly) proportional to the amount of ammonia (water) in ammonia aqueous solutions, up to a water-ammonia molar ratio of ∼75:25. Here we prove that the reactivity of these liquid mixtures is rooted in peculiar microscopic patterns emerging at the H-bonding scale, where the highly orchestrated motion of 5 solvating molecules modulates proton transfer events through local electric fields. This study demonstrates that the reaction of water with NH3 is catalyzed by a small cluster of water molecules, in which an H atom possesses a high local electric field, much like the effect observed in catalysis by water droplets [ PNAS 2023, 120, e2301206120].

CO2 Capture and Release in Amine Solutions: To What Extent Can Molecular Simulations Help Understand the Trends?

Absorption in amine solutions is a well-established advanced technology for CO2 capture. However, the fundamental aspects of the chemical reactions occurring in solution still appear to be unclear. Our previous investigation of aqueous monoethanolamine (MEA) and 2-amino-2-methyl-1,3-propanediol (AMPD), based on ab initio molecular dynamics simulations aided with metadynamics, provided new insights into the reaction mechanisms leading to CO2 capture and release with carbamate formation and dissociation. In particular, the role of water-strongly underestimated in previous computational studies-was established as essential in determining the development of all relevant reactions. In this article, we apply the same simulation protocol to other relevant primary amines, namely, a sterically hindered amine (2-amino-2-methyl-1-propanol (AMP)) and an aromatic amine (benzylamine (BZA)). We also discuss the case of CO2 capture with the formation of bicarbonate. New information is thus obtained that extends our understanding. However, quantitative predictions obtained using molecular simulations suffer from several methodological problems, and comparison among different chemical species is especially demanding. We clarify these problems further with a discussion of previous attempts to explain the different behaviors of AMP and MEA using other types of models and computations.

Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities

Graph-based descriptors, such as bond-order matrices and adjacency matrices, offer a simple and compact way of categorizing molecular structures; furthermore, such descriptors can be readily used to catalog chemical reactions (i.e., bond-making and -breaking). As such, a number of graph-based methodologies have been developed with the goal of automating the process of generating chemical reaction network models describing the possible mechanistic chemistry in a given set of reactant species. Here, we outline the evolution of these graph-based reaction discovery schemes, with particular emphasis on more recent methods incorporating graph-based methods with semiempirical and ab initio electronic structure calculations, minimum-energy path refinements, and transition state searches. Using representative examples from homogeneous catalysis and interstellar chemistry, we highlight how these schemes increasingly act as "virtual reaction vessels" for interrogating mechanistic questions. Finally, we highlight where challenges remain, including issues of chemical accuracy and calculation speeds, as well as the inherent challenge of dealing with the vast size of accessible chemical reaction space.

Combining Machine Learning Approaches and Accurate Ab Initio Enhanced Sampling Methods for Prebiotic Chemical Reactions in Solution

The study of the thermodynamics, kinetics, and microscopic mechanisms of chemical reactions in solution requires the use of advanced free-energy methods for predictions to be quantitative. This task is however a formidable one for atomistic simulation methods, as the cost of quantum-based ab initio approaches, to obtain statistically meaningful samplings of the relevant chemical spaces and networks, becomes exceedingly heavy. In this work, we critically assess the optimal structure and minimal size of an ab initio training set able to lead to accurate free-energy profiles sampled with neural network potentials. The results allow one to propose an ab initio protocol where the ad hoc inclusion of a machine-learning (ML)-based task can significantly increase the computational efficiency, while keeping the ab initio accuracy and, at the same time, avoiding some of the notorious extrapolation risks in typical atomistic ML approaches. We focus on two representative, and computationally challenging, reaction steps of the classic Strecker-cyanohydrin mechanism for glycine synthesis in water solution, where the main precursors are formaldehyde and hydrogen cyanide. We demonstrate that indistinguishable ab initio quality results are obtained, thanks to the ML subprotocol, at about 1 order of magnitude less of computational load.

A Computational Quantum-Based Perspective on the Molecular Origins of Life's Building Blocks

The search for the chemical origins of life represents a long-standing and continuously debated enigma. Despite its exceptional complexity, in the last decades the field has experienced a revival, also owing to the exponential growth of the computing power allowing for efficiently simulating the behavior of matter-including its quantum nature-under disparate conditions found, e.g., on the primordial Earth and on Earth-like planetary systems (i.e., exoplanets). In this minireview, we focus on some advanced computational methods capable of efficiently solving the Schro¨dinger equation at different levels of approximation (i.e., density functional theory)-such as ab initio molecular dynamics-and which are capable to realistically simulate the behavior of matter under the action of energy sources available in prebiotic contexts. In addition, recently developed metadynamics methods coupled with first-principles simulations are here reviewed and exploited to answer to old enigmas and to propose novel scenarios in the exponentially growing research field embedding the study of the chemical origins of life.

Successes and challenges in using machine-learned activation energies in kinetic simulations

The prediction of the thermodynamic and kinetic properties of chemical reactions is increasingly being addressed by machine-learning (ML) methods such as artificial neural networks (ANNs). While a number of recent studies have reported success in predicting chemical reaction activation energies, less attention has focused on how the accuracy of ML predictions filter through to predictions of macroscopic observables. Here, we consider the impact of the uncertainty associated with ML prediction of activation energies on observable properties of chemical reaction networks, as given by microkinetics simulations based on ML-predicted reaction rates. After training an ANN to predict activation energies given standard molecular descriptors for reactants and products alone, we performed microkinetics simulations of three different prototypical reaction networks: formamide decomposition, aldol reactions and decomposition of 3-hydroperoxypropanal. We find that the kinetic modelling predictions can be in excellent agreement with corresponding simulations performed with ab initio calculations, but this is dependent on the inherent energetic landscape of the networks. We use these simulations to suggest some guidelines for when ML-based activation energies can be reliable, and when one should take more care in applications to kinetics modelling.

Quantum Dots in Peroxidase-like Chemistry and Formamide-Based Hot Spring Synthesis of Nucleobases

Quantum dots (QDs) are usually seen as artificial semiconductor particles exhibiting optical and electronic properties interesting for nanotechnological applications. However, they may also play a role in prebiotic chemistry. Starting from zinc acetate, cadmium acetate, and mercaptosuccinic acid, we demonstrate the formation of ZnCd QDs upon UV irradiation in prebiotic liquid formamide. We show that ZnCd QDs are able to increase the yield of RNA nucleobase synthesis from formamide up to 300 times, suggesting they might have served as universal catalysts in a primordial milieu. Based on the experimentally observed peroxidase-like activity of ZnCd QDs upon irradiation with visible light, we propose that QDs could be relevant to a broad variety of processes relating to the emergence of terrestrial life.

Ab initio molecular dynamics with enhanced sampling in heterogeneous catalysis

Enhanced sampling ab initio simulations enable to study chemical phenomena in catalytic systems including thermal effects & anharmonicity, & collective dynamics describing enthalpic & entropic contributions, which can significantly impact on reaction free energy landscapes.

Structure and dynamics of nanoconfined water and aqueous solutions

This review is devoted to discussing recent progress on the structure, thermodynamic, reactivity, and dynamics of water and aqueous systems confined within different types of nanopores, synthetic and biological. Currently, this is a branch of water science that has attracted enormous attention of researchers from different fields interested to extend the understanding of the anomalous properties of bulk water to the nanoscopic domain. From a fundamental perspective, the interactions of water and solutes with a confining surface dramatically modify the liquid's structure and, consequently, both its thermodynamical and dynamical behaviors, breaking the validity of the classical thermodynamic and phenomenological description of the transport properties of aqueous systems. Additionally, man-made nanopores and porous materials have emerged as promising solutions to challenging problems such as water purification, biosensing, nanofluidic logic and gating, and energy storage and conversion, while aquaporin, ion channels, and nuclear pore complex nanopores regulate many biological functions such as the conduction of water, the generation of action potentials, and the storage of genetic material. In this work, the more recent experimental and molecular simulations advances in this exciting and rapidly evolving field will be reported and critically discussed.

The Prebiotic Kitchen: A Guide to Composing Prebiotic Soup Recipes to Test Origins of Life Hypotheses

“Prebiotic soup” often features in discussions of origins of life research, both as a theoretical concept when discussing abiological pathways to modern biochemical building blocks and, more recently, as a feedstock in prebiotic chemistry experiments focused on discovering emergent, systems-level processes such as polymerization, encapsulation, and evolution. However, until now, little systematic analysis has gone into the design of well-justified prebiotic mixtures, which are needed to facilitate experimental replicability and comparison among researchers. This paper explores principles that should be considered in choosing chemical mixtures for prebiotic chemistry experiments by reviewing the natural environmental conditions that might have created such mixtures and then suggests reasonable guidelines for designing recipes. We discuss both “assembled” mixtures, which are made by mixing reagent grade chemicals, and “synthesized” mixtures, which are generated directly from diversity-generating primary prebiotic syntheses. We discuss different practical concerns including how to navigate the tremendous uncertainty in the chemistry of the early Earth and how to balance the desire for using prebiotically realistic mixtures with experimental tractability and replicability. Examples of two assembled mixtures, one based on materials likely delivered by carbonaceous meteorites and one based on spark discharge synthesis, are presented to illustrate these challenges. We explore alternative procedures for making synthesized mixtures using recursive chemical reaction systems whose outputs attempt to mimic atmospheric and geochemical synthesis. Other experimental conditions such as pH and ionic strength are also considered. We argue that developing a handful of standardized prebiotic recipes may facilitate coordination among researchers and enable the identification of the most promising mechanisms by which complex prebiotic mixtures were “tamed” during the origin of life to give rise to key living processes such as self-propagation, information processing, and adaptive evolution. We end by advocating for the development of a public prebiotic chemistry database containing experimental methods (including soup recipes), results, and analytical pipelines for analyzing complex prebiotic mixtures.

Chemically Accurate Vibrational Free Energies of Adsorption from Density Functional Theory Molecular Dynamics: Alkanes in Zeolites

We present a methodology to compute, at reduced computational cost, Gibbs free energies, enthalpies, and entropies of adsorption from molecular dynamics. We calculate vibrational partition functions from vibrational energies, which we obtain from the vibrational density of states by projection on the normal modes. The use of a set of well-chosen reference structures along the trajectories accounts for the anharmonicities of the modes. For the adsorption of methane, ethane, and propane in the H-CHA zeolite, we limit our treatment to a set of vibrational modes localized at the adsorption site (zeolitic OH group) and the alkane molecule interacting with it. Only two short trajectories (1–20 ps) are required to reach convergence (<1 kJ/mol) for the thermodynamic functions. The mean absolute deviations from the experimentally measured values are 2.6, 2.8, and 4.7 kJ/mol for the Gibbs free energy, the enthalpy, and the entropy term (−TΔS), respectively. In particular, the entropy terms show a major improvement compared to the harmonic approximation and almost reach the accuracy of the previous use of anharmonic frequencies obtained with curvilinear distortions of individual modes. The thermodynamic functions so obtained follow the trend of the experimental values for methane, ethane, and propane, and the Gibbs free energy of adsorption at experimental conditions is correctly predicted to change from positive for methane (5.9 kJ/mol) to negative for ethane (−4.8 kJ/mol) and propane (−7.1 kJ/mol).

Unsupervised Learning Methods for Molecular Simulation Data

Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry. In this Review, we provide a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicate likely directions for further developments in the field. In particular, we discuss feature representation of molecular systems and present state-of-the-art algorithms of dimensionality reduction, density estimation, and clustering, and kinetic models. We divide our discussion into self-contained sections, each discussing a specific method. In each section, we briefly touch upon the mathematical and algorithmic foundations of the method, highlight its strengths and limitations, and describe the specific ways in which it has been used-or can be used-to analyze molecular simulation data.

The Beginning of HCN Polymerization: Iminoacetonitrile Formation and Its Implications in Astrochemical Environments

Hydrogen cyanide (HCN) is known to react with complex organic materials and is a key reagent in the formation of various prebiotic building blocks, including amino acids and nucleobases. Here, we explore the possible first step in several such processes, the dimerization of HCN into iminoacetonitrile. Our study combines steered ab initio molecular dynamics and quantum chemistry to evaluate the kinetics and thermodynamics of base-catalyzed dimerization of HCN in the liquid state. Simulations predict a formation mechanism of iminoacetonitrile that is consistent with experimentally observed time scales for HCN polymerization, suggesting that HCN dimerization may be the rate-determining step in the assembly of more complex reaction products. The predicted kinetics permits for iminoacetonitrile formation in a host of astrochemical environments, including on the early Earth, on periodically heated subsurfaces of comets, and following heating events on colder bodies, such as Saturn's moon Titan.

Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network

Interfacial reactions are notoriously difficult to characterize, and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid-electrolyte interphase (SEI), critical to Li-ion batteries (LIBs), exemplifies such a surface film, and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. Here we use a reaction network to investigate whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic reaction landscape, calculated with density functional theory. The shortest pathfinding algorithms are employed to explore the reaction landscape and obtain previously proposed formation mechanisms of LEMC as well as several new reaction pathways and intermediates. For example, we identify two novel LEMC formation mechanisms: one which involves LiH generation and another that involves breaking the (CH₂)O-C(═O)OLi bond in LEDC. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared with LEDC, a conclusion that has direct impact on the SEI formation in Li-ion energy storage systems. Finally, the data-driven framework presented here is generally applicable to any electrochemical system and expected to improve our understanding of surface passivation.

Plasma Modeling and Prebiotic Chemistry: A Review of the State-of-the-Art and Perspectives

We review the recent progress in the modeling of plasmas or ionized gases, with compositions compatible with that of primordial atmospheres. The plasma kinetics involves elementary processes by which free electrons ultimately activate weakly reactive molecules, such as carbon dioxide or methane, thereby potentially starting prebiotic reaction chains. These processes include electron–molecule reactions and energy exchanges between molecules. They are basic processes, for example, in the famous Miller-Urey experiment, and become relevant in any prebiotic scenario where the primordial atmosphere is significantly ionized by electrical activity, photoionization or meteor phenomena. The kinetics of plasma displays remarkable complexity due to the non-equilibrium features of the energy distributions involved. In particular, we argue that two concepts developed by the plasma modeling community, the electron velocity distribution function and the vibrational distribution function, may unlock much new information and provide insight into prebiotic processes initiated by electron–molecule collisions.

Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps

Enhanced sampling techniques have become an essential tool in computational chemistry and physics, where they are applied to sample activated processes that occur on a time scale that is inaccessible to conventional simulations. Despite their popularity, it is well known that they have constraints that hinder their application to complex problems. The core issue lies in the need to describe the system using a small number of collective variables (CVs). Any slow degree of freedom that is not properly described by the chosen CVs will hinder sampling efficiency. However, the exploration of configuration space is also hampered by including variables that are not relevant for the activated process under study. This paper presents the Adaptive Topography of Landscape for Accelerated Sampling (ATLAS), a new biasing method capable of working with many CVs. The root idea of ATLAS is to apply a divide-and-conquer strategy, where the high-dimensional CVs space is divided into basins, each of which is described by an automatically determined, low-dimensional set of variables. A well-tempered metadynamics-like bias is constructed as a function of these local variables. Indicator functions associated with the basins switch on and off the local biases so that the sampling is performed on a collection of low-dimensional CV spaces that are smoothly combined to generate an effectively high-dimensional bias. The unbiased Boltzmann distribution is recovered through reweighing, making the evaluation of conformational and thermodynamic properties straightforward. The decomposition of the free-energy landscape in local basins can be updated iteratively as the simulation discovers new (meta)stable states.

Stability of a Monoethanolamine-CO2 Zwitterion at the Vapor/Liquid Water Interface: Implications for Low Partial Pressure Carbon Capture Technologies

The need to chemically convert CO₂ at the interface of aqueous amine solutions has become particularly relevant for the development and the broad distribution of cost-effective and near-future devices for direct air capture working at low (e.g., ambient) partial pressure. Here, we have determined the stability of a CO₂-monoethanolamine zwitterion and its chemical conversion into carbamate at the vapor/liquid water interface by first-principles molecular dynamics simulations coupled with a recently introduced enhanced sampling technique. Contrary to the bulk water case, our results show that both the zwitterion and carbamate ions are poorly stable at the vapor/amine aqueous interface, further stating the differences between the homogeneous and heterogeneous CO₂ chemical conversion. The design of novel and cost-effective capture systems, such as those offered by amine-based scrubbing solutions, working at low (e.g., ambient) CO₂ partial pressure should explore the use of novel solvents, different from aqueous mixtures, to overcome the limits of the current absorbents.

Promotion Energy Analysis Predicts Reaction Modes: Nucleophilic and Electrophilic Aromatic Substitution Reactions

To develop an approach to pre-emptively predict the existence of major reaction modes associated with a chemical system, based on exclusive consideration of reactant properties, we build herein on the valence bond perspective of chemical reactivity. In this perspective, elementary chemical reactions are conceptualized as crossovers between individual diabatic/semilocalized states. As demonstrated, the spacings between the main diabatic states in the reactant geometries-the so-called promotion energies-contain predictive information about which types of crossings are likely to occur on a potential energy surface, facilitating the identification of potential transition states and products. As an added bonus, promotion energy analysis provides direct insight into the impact of environmental effects, e.g., the presence of (polar) solvents and/or (local) electric fields, on a mechanistic landscape. We illustrate the usefulness of our approach by focusing on model nucleophilic and electrophilic aromatic substitution reactions. Overall, we envision our analysis to be useful not only as a tool for conceptualizing individual mechanistic landscapes but also as a facilitator of systematic reaction-network exploration efforts. Because the emerging VB descriptors are computationally inexpensive (and can alternatively be inferred through machine learning), they could be evaluated on-the-fly as part of an exploration algorithm. The so-predicted reaction modes could subsequently be examined in detail through computationally more-demanding methods.

Step by Step Strecker Amino Acid Synthesis from Ab Initio Prebiotic Chemistry

The amino acids synthesis from elementary precursors in abiotic conditions is traditionally described according to the Strecker reaction, thoroughly invoked to justify the observation of amino acids in extraterrestrial samples and their emergence in the primordial Earth. To this day, however, a quantitative microscopic description of the mechanism, thermodynamics, and kinetics of the multistep Strecker reaction is still lacking. In the present work we tackle this study by adopting a state-of-the-art ab initio computational approach, combining an efficient scheme of exploration of the relevant chemical networks with a rigorous determination of the underlying free energy and transition states. We determine the step-by-step chemical pathway from "Strecker precursors" to glycine in solution and calculate the corresponding full free energy landscape. Our results agree well with the scarce available experimental data and complete them, thus providing the first end-to-end study of this complex reaction, a crucial bottleneck for the emergence of life.

Prebiotic Organic Chemistry of Formamide and the Origin of Life in Planetary Conditions: What We Know and What Is the Future

The goal of prebiotic chemistry is the depiction of molecular evolution events preceding the emergence of life on Earth or elsewhere in the cosmos. Plausible experimental models require geochemical scenarios and robust chemistry. Today we know that the chemical and physical conditions for life to flourish on Earth were at work much earlier than thought, i.e., earlier than 4.4 billion years ago. In recent years, a geochemical model for the first five hundred million years of the history of our planet has been devised that would work as a cradle for life. Serpentinization processes in the Hadean eon affording self-assembled structures and vesicles provides the link between the catalytic properties of the inorganic environment and the impressive chemical potential of formamide to produce complete panels of organic molecules relevant in pre-genetic and pre-metabolic processes. Based on an interdisciplinary approach, we propose basic transformations connecting geochemistry to the chemistry of formamide, and we hint at the possible extension of this perspective to other worlds.

How natural materials remove heavy metals from water: mechanistic insights from molecular dynamics simulations

Water pollution by heavy metals is of increasing concern due to its devastating effects on the environment and on human health. For the removal of heavy metals from water sources, natural materials, such as spent-coffee-grains or orange/banana/chestnut peels, appear to offer a potential cheap alternative to more sophisticated and costly technologies currently in use. However, in order to employ them effectively, it is necessary to gain a deeper understanding – at the molecular level – of the heavy metals-bioorganic-water system and exploit the power of computer simulations. As a step in this direction, we investigate via atomistic simulations the capture of lead ions from water by hemicellulose – the latter being representative of the polysaccharides that are common components of vegetables and fruit peels − as well as the reverse process. A series of independent molecular dynamics simulations, both classical and ab initio, reveals a coherent scenario which is consistent with what one would expect of an efficient capture, i.e. that it be fast and irreversible: (i) binding of the metal ions via adsorption is found to happen spontaneously on both carboxylate and hydroxide functional groups; (ii) in contrast, metal ion desorption, leading to solvation in water, involves sizable free-energy barriers.

We investigate via atomistic simulations the capture of lead ions from water by hemicellulose – as representative of the polysaccharides that are common components of vegetables and fruit peels – and the reverse process.

Theoretical investigation of defective MXenes as potential electrocatalysts for CO reduction toward C2 products

Electrochemical CO2/CO conversion to valuable chemical products is an attractive strategy for storage of clean energy and control of greenhouse gas emission. Currently, CO2 reduction to CO is relatively mature, whereas the deep reduction and further conversion of CO into multi-carbon products, such as ethylene (C2H4) and ethanol (C2H5OH), are highly challenging. Based on the density functional theory (DFT) calculations, we explored the possibility of CO reduction reaction (CORR), to obtain C2 products, with defective MXenes in which the defect is created by removing two neighboring oxygen atoms on the surface. Our results revealed that the dual-oxygen vacancy in defective Mo2TiC2O2 (labeled as Mo2TiC2O2-2OV) can offer a unique environment that confines and enriches the active *COH species, significantly promoting the reduction process as well as C-C bond coupling. The thermodynamic barrier of the potential-determining step (PDS) for Mo2TiC2O2-2OV is 0.32 eV with promising selectivity of C2 products over the competing hydrogen evolution reaction (HER). This work provides a feasible strategy for designing MXene-based electrocatalysts for highly efficient CO2/CO reduction to C2 products.

Free energy barriers from biased molecular dynamics simulations

Atomistic simulation methods for the quantification of free energies are in wide use. These methods operate by sampling the probability density of a system along a small set of suitable collective variables (CVs), which is, in turn, expressed in the form of a free energy surface (FES). This definition of the FES can capture the relative stability of metastable states but not that of the transition state because the barrier height is not invariant to the choice of CVs. Free energy barriers therefore cannot be consistently computed from the FES. Here, we present a simple approach to calculate the gauge correction necessary to eliminate this inconsistency. Using our procedure, the standard FES as well as its gauge-corrected counterpart can be obtained by reweighing the same simulated trajectory at little additional cost. We apply the method to a number of systems-a particle solvated in a Lennard-Jones fluid, a Diels-Alder reaction, and crystallization of liquid sodium-to demonstrate its ability to produce consistent free energy barriers that correctly capture the kinetics of chemical or physical transformations, and discuss the additional demands it puts on the chosen CVs. Because the FES can be converged at relatively short (sub-ns) time scales, a free energy-based description of reaction kinetics is a particularly attractive option to study chemical processes at more expensive quantum mechanical levels of theory.

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.

Chemical evolution: from formamide to nucleobases and amino acids without the presence of catalyst

Abiotic synthesis of nucleobases and amino acids is of critical importance as it sheds light on potential prebiotic chemical reactions. During thermal decomposition of formamide in vacuum conditions, purine, cytosine, adenine, hypoxanthine, uracil, pterin, urea, urocanic acid, glycine, alanine and norvaline were detected. The compounds were obtained without catalyst by heating at 100-180 °C or microwave heating of formamide. Reaction network of self-catalyzed chemical reactions is suggested, showing how from only one parent molecule, nucleobases, urea and the amino acid glycine can be produced. The reaction pathways are theoretically determined using SCS-MP2 calculations.Communicated by Ramaswamy H. Sarma.

Efficient computation of free energy surfaces of chemical reactions using ab initio molecular dynamics with hybrid functionals and plane waves

Ab initio molecular dynamics (AIMD) simulations employing density functional theory (DFT) and plane waves are routinely carried out using density functionals at the level of generalized gradient approximation (GGA). AIMD simulations employing hybrid density functionals are of great interest as it offers a more accurate description of structural and dynamic properties than the GGA functionals. However, the computational cost for carrying out calculations using hybrid functionals and plane wave basis set is at least two orders of magnitude higher than that using GGA functionals. Recently, we proposed a strategy that combined the adaptively compressed exchange operator formulation and the multiple time step integration scheme to reduce the computational cost by an order of magnitude [J. Chem. Phys. 151, 151102 (2019)]. In this work, we demonstrate the application of this method to study chemical reactions, in particular, formamide hydrolysis in an alkaline aqueous medium. By actuating our implementation with the well-sliced metadynamics scheme, we can compute the two-dimensional free energy surface of this reaction at the level of hybrid-DFT. This work also investigates the accuracy of the PBE0 (hybrid) and the PBE (GGA) functionals in predicting the free energetics of this chemical reaction.

Monitoring the Reactivity of Formamide on Amorphous SiO2 by In-Situ UV-Raman Spectroscopy and DFT Modeling

Formamide has been recognized in the literature as a key species in the formation of the complex molecules of life, such as nucleobases. Furthermore, several studies reported the impact of mineral phases as catalysts for its decomposition/polymerization processes, increasing the conversion and also favoring the formation of specific products. Despite the progresses in the field, in situ studies on these mineral-catalyzed processes are missing. In this work, we present an in situ UV-Raman characterization of the chemical evolution of formamide over amorphous SiO₂ samples, selected as a prototype of silicate minerals. The experiments were carried out after reaction of formamide at 160 °C on amorphous SiO₂ (Aerosil OX50) either pristine or pre-calcined at 450 °C, to remove a large fraction of surface silanol groups. Our measurements, interpreted on the basis of density functional B3LYP-D3 calculations, allow to assign the spectra bands in terms of specific complex organic molecules, namely, diaminomaleonitrile (DAMN), 5-aminoimidazole (AI), and purine, showing the role of the mineral surface on the formation of relevant prebiotic molecules.

A quantum mechanical study of dehydration vs. decarbonylation of formamide catalysed by amorphous silica surfaces

Formamide is abundant in the interstellar medium and was also present during the formation of the Solar system through the accretion process of interstellar dust. Under the physicochemical conditions of primordial Earth, formamide could have undergone decomposition, either via dehydration (HCN + H2O) or via decarbonylation (CO + NH3). The first reactive channel provides HCN, which is an essential molecular building block for the formation of RNA/DNA bases, crucial for the emergence of life on Earth. In this work, we studied, at the CCSD(T)/cc-pVTZ level, the two competitive routes of formamide decomposition, i.e. dehydration and decarbonylation, either in liquid formamide (by using the polarization continuum model technique) or at the interface between liquid formamide and amorphous silica. Amorphous silica was adopted as a convenient model of the crystalline silica phases ubiquitously present in the primordial (and actual) Earth's crust, and also due to its relevance in catalysis, adsorption and chromatography. Results show that: (i) silica surface sites catalyse both decomposition channels by reducing the activation barriers by about 100 kJ mol-1 with respect to the reactions in homogeneous medium, and (ii) the dehydration channel, giving rise to HCN, is strongly favoured from a kinetic standpoint over decarbonylation, the latter being, instead, slightly favoured from a thermodynamic point of view.

Carbon dioxide, bicarbonate and carbonate ions in aqueous solutions under deep Earth conditions

We investigate the effect of pressure, temperature and acidity on the composition of water-rich carbon-bearing fluids under thermodynamic conditions that correspond to the Earth's deep crust and upper mantle. Our first-principles molecular dynamics simulations provide mechanistic insight into the hydration shell of carbon dioxide, bicarbonate and carbonate ions, and into the pathways of the acid/base reactions that convert these carbon species into one another in aqueous solutions. At temperatures of 1000 K and higher, our simulations can sample the chemical equilibrium of these acid/base reactions, thus allowing us to estimate the chemical composition of diluted carbon dioxide and (bi)carbonate ions as a function of acidity and thermodynamic conditions. We find that, especially at the highest temperature, the acidity of the solution is essential to determine the stability domain of CO₂vs. HCO₃^-vs. CO₃^2-.