Potential Energy Landscape Research Articles

Potential energy landscape formalism for quantum molecular liquids

The potential energy landscape (PEL) formalism is a powerful tool within statistical mechanics to study the thermodynamic properties of classical low-temperature liquids and glasses. Recently, the PEL formalism has been extended to liquids/glasses that obey quantum mechanics, but applications have been limited to atomistic model liquids. In this work, we extend the PEL formalism to liquid/glassy water using path-integral molecular dynamics (PIMD) simulations, where nuclear quantum effects (NQE) are included. Our PIMD simulations, based on the q-TIP4P/F water model, show that the PEL of quantum water is both Gaussian and anharmonic. Importantly, the ring-polymers associated to the O/H atoms in the PIMD simulations, collapse at the local minima of the PEL (inherent structures, IS) for both liquid and glassy states. This allows us to calculate, analytically, the IS vibrational density of states (IS-VDOS) of the ring-polymer system using the IS-VDOS of classical water (obtained from classical MD simulations). The role of NQE on the structural properties of liquid/glassy water at various pressures are discussed in detail. Overall, our results demonstrate that the PEL formalism can effectively describe the behavior of molecular liquids at low temperatures and in the glass states, regardless of whether the liquid/glass obeys classical or quantum mechanics.

Medium-density amorphous ice unveils shear rate as a new dimension in water’s phase diagram

Recent experiments revealed a new amorphous ice phase, medium-density amorphous ice (MDA), formed by ball-milling ice Ih at 77 K [Rosu-Finsen et al., Science 379, 474-478 (2023)]. MDA has density between that of low-density amorphous (LDA) and high-density amorphous (HDA) ices, adding to the complexity of water's phase diagram, known for its glass polyamorphism and two-state thermodynamics. The nature of MDA and its relation to other amorphous ices and liquid water remain unsolved. Here, we use molecular simulations under controlled pressure and shear rate at 77 K to produce and investigate MDA. We find that MDA formed at constant shear rate is a steady-state nonequilibrium shear-driven amorphous ice (SDA), that can be produced by shearing ice Ih, LDA, or HDA. Our results suggest that MDA could be obtained by ball-milling water glasses without crystallization interference. Increasing the shear rate at ambient pressure produces SDAs with densities ranging from LDA to HDA, revealing shear rate as a new thermodynamic variable in the nonequilibrium phase diagram of water. Indeed, shearing provides access to amorphous states inaccessible by controlling pressure and temperature alone. SDAs produced with shearing rates as high as 106 s-1 sample the same region of the potential energy landscape than hyperquenched glasses with identical density, pressure, and temperature. Intriguingly, SDAs obtained by shearing at ~108 s-1 have density, enthalpy, and structure indistinguishable from those of water "instantaneously" quenched from room temperature to 77 K over 10 ps, making them good approximants for the "true glass" of ambient liquid water.

Molecular Insights Into β-Glucuronidase Inhibition by Alhagi Graecorum Flavonoids: A Computational and Experimental Approach.

In this study, we aimed to investigate the inhibitory mechanisms of β-glucuronidase by flavonoids derived from Alhagi graecorum through both experimental and computational approaches. The activity of β-glucuronidase was assessed using an in vitro enzyme inhibition assay, where myricetin and chrysoeriol were identified as potent inhibitors based on their low IC50 values. Kinetic studies were conducted to determine the inhibition type, revealing that both compounds exhibit noncompetitive inhibition of β-glucuronidase-catalyzed hydrolysis of PNPG. Molecular docking was employed to explore the binding affinities of the flavonoids, showing that myricetin formed the highest number of polar interactions with the enzyme. Additionally, molecular dynamics (MD) simulations were performed to evaluate the stability of the enzyme-inhibitor complexes, demonstrating consistent trajectory behavior for both compounds, with significant energy stabilization. Interaction energy analyses highlighted the dominant role of electrostatic forces in myricetin's inhibition mechanism, while Van der Waals forces were more prominent for chrysoeriol. The MM/PBSA method was used to calculate the binding free energies, with myricetin and chrysoeriol exhibiting the lowest values. Potential energy landscape analysis further revealed that β-glucuronidase adopts a more closed conformation when bound to these inhibitors, limiting substrate access. These findings suggest that flavonoids from Alhagi graecorum hold promise for clinical applications, particularly in managing drug-induced enteropathy.

Simple Models of Directly Measured Energy Landscapes for Different Shaped Particles in Nonuniform AC Electric Fields.

We report direct measurements of potential energy landscapes for different shaped colloidal particles interacting with nonuniform AC electric fields. Epoxy particle shapes investigated include disks, ellipses, squares, rectangles, and rhombuses, which are all part of the superelliptical prism shape class and are chosen to systematically vary particle anisotropy and corner features. The measurement configuration consists of noninteracting single particles sedimented onto microscope slides within electric fields between parallel coplanar electrodes. Thermally sampled positions and orientations of single particles in nonuniform fields are tracked in an optical microscope, and measured potential energy landscapes are obtained via Boltzmann inversions. We develop a new analytically simple model that captures all measured energy landscapes for superelliptical prism shaped colloidal particles with electrostatic double layers. The model recovers known validated potentials for spherical and ellipsoidal particles, and therefore captures energy landscapes for a variety of different colloidal particle sizes, shapes, and materials reported in prior studies.

Infinitely rugged intra-cage potential energy landscape in metallic glasses caused by many-body interaction

The absence of translational symmetry in glassy materials poses a significant challenge in establishing effective structure-property relationships in real space. Consequently, the potential energy landscape (PEL) in phase space is widely utilized to comprehend the complex phenomena in glasses. The classical PEL features a two-scale profile comprising mega-basins and sub-basins, corresponding to α-relaxations (e.g. glass transition) and β-relaxations (e.g. local cage-breaking atomic rearrangements), respectively. Recent studies, however, reveal that sub-basins are not smooth and contain finer structures, the origins of which remain elusive. Here we probe the smoothness of sub-basin bottoms in glasses' PEL by introducing small intra-cage cyclic loading and then measuring the net changes in atomic-level stresses. Compared to glasses with pair interaction, glasses with many-body interaction exhibit orders-of-magnitude larger and loading-dependent stress changes even before the first cage-breaking event takes place, which reflect much more feature-rich sub-basins. We further demonstrate this stark contrast stems from the spatial distribution of individual atom's constraining force field. Specifically, at vanishing perturbations, many-body interactions disrupt the positive-definite synchrony in energy variations of the perturbed atom and the whole system, causing inherently less confined atomic responses and infinitely rugged sub-basins. The implications of these findings for the selective addition or removal of fine structures in the PEL and the subsequent tuning of glassy materials' responses to external stimuli are also explored.

Phase transitions of ice VIII-VII-X: A potential energy landscape perspective

Water ice, an archetypal molecular system, exhibits a complex phase diagram characterized by numerous phase transitions under varying pressure-temperature conditions. However, it remains a significant challenge to theoretical modeling of these transitions owing to the high dimensionality in representing the potential energy landscapes (PEL) of ice crystalline phases. In an attempt to deeply understand the mechanisms of phase transitions between ice VII, VIII, and X, a graph-based stationary-point network was employed to simplify the representation of the PEL and locally constructed the PEL of ice at different pressures by our developed high-index saddle dynamic scheme. The stationary-point networks at 40 and 80 GPa indicate a pronounced flattened PEL of ice over a wide range of pressure (up to at least 80 GPa), where multiple structures (i.e., local minima) are energetically degenerate with phase VIII and separated by low-energy barriers (∼10 meV/atom at 40 GPa). These features indicate the disordered VII phase is more favorable at finite temperatures, in agreement with previous experimental observations. Additionally, we demonstrated that the PEL topology undergoes a fundamental alteration at higher pressures, with a single funnel emerging at 120 GPa, facilitating the crystallization of phase X. This work serves as a proof of concept for using the stationary-point network to explicitly represent the PEL, highlighting their potential in elucidating complex phase transitions. Published by the American Physical Society 2024

Correlative activation free energy (CAFE) model: Application to viscous processes in inorganic oxide glass-formers

We explore the concept of correlative activation free energy (CAFE) for the analysis and interpretation of viscosity data of a collection of 847 inorganic oxide glass formers that exhibit various degrees of non-Arrhenius behavior. The CAFE model formalism is strictly based on transition state theory and accounts for the variation in the activation barrier height for the viscous dissipation due to the structural evolution the system undergoes upon traversing the glass transition regime. Thus, fitting parameters are meaningful in a statistical thermodynamic context. Compared to the VFT and MYEGA equations, fits using the CAFE model are more robust when extrapolating to infinite temperature because the latter encodes non-enthalpic contributions to the rate coefficient more realistically. The CAFE model-based analysis reveals a strong connection between melt fragility and the degree of change in the potential energy landscape with temperature. Accordingly, while the average ground-state potential energy of the glass forming liquid gradually increases with temperature, the energy of the activated state remains relatively invariant. Our analysis also allows one to estimate the number of atoms involved in the viscous relaxation process, which ranges from approximately 10 to 50 atoms for the oxide glass formers studied. We observe that thermally activated dynamic phenomena exhibited by glassy networks, such as the mixed alkali effect, persist into the supercooled liquid state.

DNA methylation stochasticity is linked to transcriptional variability and identifies convergent epigenetic disruption across genetically-defined subtypes of AML.

Disruption of the epigenetic landscape is of particular interest in acute myeloid leukemia (AML) due to its relatively low mutational burden and frequent occurrence of mutations in epigenetic regulators. Here, we applied an information-theoretic analysis of methylation potential energy landscapes, capturing changes in mean methylation level and methylation entropy, to comprehensively analyze DNA methylation stochasticity in subtypes of AML defined by mutually exclusive genetic mutations. We identified AML subtypes with CEBPA double mutation and those with IDH mutations as distinctly high-entropy subtypes, marked by methylation disruption over a convergent set of genes. We found a core program of epigenetic landscape disruption across all AML subtypes, with discordant methylation stochasticity and transcriptional dysregulation converging on functionally important leukemic signatures, suggesting a genotype-independent role of stochastic disruption of the epigenetic landscape in mediating leukemogenesis. We further established a relationship between methylation entropy and gene expression variability, connecting the disruption of the epigenetic landscape to transcription in AML. This approach identified a convergent program of epigenetic dysregulation in leukemia, clarifying the contribution of specific genetic mutations to stochastic disruption of the epigenetic and transcriptional landscapes of AML.

Exploring Nanocluster Potential Energy Surfaces via Deep Reinforcement Learning: Strategies for Global Minimum Search.

The search for global minimum (GM) configurations in nanoclusters is complicated by intricate potential energy landscapes replete with numerous local minima. The complexity of these landscapes escalates with increasing cluster size and compositional diversity. Evolutionary algorithms, such as genetic algorithms, are hampered by slow convergence rates and a propensity for prematurely settling on suboptimal solutions. Likewise, the basin hopping technique faces difficulties in navigating these complex landscapes effectively, particularly at larger scales. These challenges highlight the need for more sophisticated methodologies to efficiently scan the potential energy surfaces of nanoclusters. In response, our research has developed a novel deep reinforcement learning (DRL) framework specifically designed to explore the potential energy surfaces (PES) of nanoclusters, aiming to identify the GM configurations along with other low-energy states. This study demonstrates the framework's effectiveness in managing various nanocluster types, including both mono- and multimetallic compositions, and its proficiency in navigating complex energy landscapes. The model is characterized by remarkable adaptability and sustained efficiency, even as cluster sizes and feature vector dimensions increase. The demonstrated adaptability of DRL in this context underscores its considerable potential in materials science, particularly for the efficient discovery and optimization of novel nanomaterials. To the best of our knowledge, this is the first DRL framework designed for the GM search in nanoclusters, representing a significant innovation in the field.

Multistable Composite Laminate Grids as a Design Tool for Soft Reconfigurable Multirotors

Adaptive‐morphology multirotors exhibit superior versatility and task‐specific performance compared to traditional multirotors owing to their functional morphological adaptability. However, a notable challenge lies in the contrasting requirements of locking each morphology for flight controllability and efficiency while permitting low‐energy reconfiguration. A novel design approach is proposed for reconfigurable multirotors utilizing soft multistable composite laminate airframes. These airframes show kinematically determinate morphologies corresponding to multiple minima in their elastic potential energy landscape. By varying design parameters, the methodology allows for tuning the energy landscape characteristics governing each morphology's structural stability and reconfiguration energetics. The airframe, composed of multistable composite laminate grids, is optimized to maximize rigidity under flight loads and minimize reconfiguration work. The 130‐g reconfigurable multirotor design demonstrates self‐locking properties in an open and a folded configuration, enabling a 48% reduction in width‐span without compromising stability during flight. Soft pneumatic actuators, actuated using a tethered compressed air supply, enable reversible reconfiguration on the ground between open and folded configurations. The design resolves the conflicting requirements of high‐stiffness to lock each flight configuration and low‐actuation work for reconfigurability. By exploiting soft yet multistable structures, the approach combines the stability observed in rigid‐linked reconfigurable multirotors with the low‐effort reconfigurability of soft multirotors, offering new methods for designing adaptive‐morphology multirotors.

Infrared Spectroscopy and Photochemistry of Ethyl Maltol in Low-Temperature Argon Matrix

Ethyl maltol was investigated using matrix isolation infrared spectroscopy and DFT calculations. In an argon matrix (14.5 K), the compound was found to exist in a single conformer (form I), characterized by an intramolecular hydrogen bond with an estimated energy of ~17 kJ mol−1. The IR spectrum of this conformer was assigned, and the molecule’s potential energy landscape was explored to understand the relative stability and isomerization dynamics of the conformers. Upon annealing the matrix to 41.5 K, ethyl maltol was found to predominantly aggregate into a centrosymmetric dimer (2× conformer I) bearing two intermolecular hydrogen bonds with an estimated energy of ca. 28 kJ mol−1 (per bond). The UV-induced (λ > 235 nm) photochemistry of the matrix-isolated ethyl maltol was also investigated. After 1 min of irradiation, band markers of two rearrangement photoproducts formed through the photoinduced detachment-attachment (PIDA) mechanism, in which the ethyl maltol radical acts as an intermediate, were observed: 1-ethyl-3-hydroxy-6-oxibicyclo [3.1.0] hex-3-en-2-one and 2-ethyl-2H-pyran-3,4-dione. The first undergoes subsequent reactions, rearranging to 4-hydroxy-4-propanoylcyclobut-2-en-1-one and photofragmenting to cyclopropenone and 2-hydroxybut-1-en-1-one. Other final products were also observed, specifically acetylene and CO (the expected fragmentation products of cyclopropenone), and CO2. Overall, the study demonstrated ethyl maltol’s high reactivity under UV irradiation, with significant photochemical conversion occurring within minutes. The rapid photochemical conversion, with complete consumption of the compound in 20 min, should be taken into account in designing practical applications of ethyl maltol.

Photo-induced loss of H2 from H2S, CH4, H2O and SiH4, when and why is it possible

We have explored the possibility of forming H⋅ and H2 from H2S, CH4, H2O and SiH4 in a series of ab initio calculations. The key finding is that H2S can give rise to direct photolytic ultrafast formation of H2 This finding is in agreement with the available experimental data and is a result of a directed pathway that leads toward H2 formation at easily accessible wavelengths. It arises at the (conical) intersection between the two lowest-lying electronic states. This intersection is a result of the valence orbital interaction of the two hydrogen atoms. This interaction is becoming more favorable as the S-H bonds simultaneously becomes longer. The H-S σ-σ∗ bonding interaction cannot compete with the bond formation that takes place between the hydrogens as two bonds stretch simultaneously. For oxygen this interaction it predominant. The reason is that sulfur has a higher principal quantum number and the interaction with the 1s of hydrogen therefore is less favorable. The excited states that are involved in the H2 formation from H2S could be populated by, for example, a two-photon 400 nm excitation. The other hydrides have a more entangled potential energy landscape close to the Franck-Condon region in the direction of the reaction coordinate that leads to H2 loss and as a result the direct loss of a hydrogen atom is favored. A two-photon excitation could be induced by femtosecond laser pulses that at the same time is proposed as a future platform for sensing the H2S molecules with the backward transient absorption scheme. The implication would be that both sensing and photoinduced H2 formation could be in place at the same time which justifies the use of expensive femtosecond light sources getting “two for the price of one”.

A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions.

The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and machine learning (ML) schemes are employed to obtain CVs from molecular dynamics (MD)/Monte Carlo (MC) trajectories or structural databanks for biomolecules. However, minimum sampling conditions to generate reliable CVs that accurately describe the underlying energy landscape remain unclear. Here, we address this issue by developing a Mode evolution Metric (MeM) to extract CVs that can pinpoint new states and describe local transitions in the vicinity of a reference minimum from nonequilibrated MD/MC trajectories. We present a general mathematical formulation of MeM for both statistical dimensionality reduction and machine learning approaches. Application of MeM to MC trajectories of model potential energy landscapes and MD trajectories of solvated alanine dipeptide reveals that the principal components which locate new states in the vicinity of a reference minimum emerge well before the trajectories locally equilibrate between the associated states. Finally, we demonstrate a possible application of MeM in designing efficient biased sampling schemes to construct accurate energy landscape slices that link transitions between states. MeM can help speed up the search for new minima around a biomolecular conformational state and enable the accurate estimation of thermodynamics for states lying on the energy landscape and the description of associated transitions.

Moiré-pattern-tuned interlayer friction of two-dimensional α- and β-tellurene via first-principles calculations and experimental validation

Tellurene, as an emerging class of two-dimensional (2D) materials, exhibit distinctive physicochemical properties derived from their varied structural morphologies, particularly in van der Waals (vdW) heterostructures formed between their allotropes, demonstrating substantial potential for superlubricity applications. This study employs a synergistic approach that combines theoretical calculations with experimental investigations to investigate the tribological performance of two tellurene allotropes (α- and β-Te) and their allotropic homojunctions (α-Te/β-Te). The frictional forces of the tellurene systems under varying twist angles were predicted by applying potential energy surfaces (PESs) to the Prandtl–Tomlinson (PT) model. These findings indicate that a reduction in energy barriers leads to decreased frictional forces, thereby enhancing the system's superlubricity. Notably, the frictional response is influenced not only by the interlayer sliding barriers but also by the shape and periodicity of the potential energy landscapes. Furthermore, leveraging the concept of the lubricating figure of merit, this study provides an in-depth analysis of the intrinsic frictional characteristics within a tellurene system. Experimentally, few-layer α- and β-Te tellurene were successfully synthesized, and their interlayer frictional properties were measured, showing high congruence with theoretical predictions. The outcomes reveal the exceptional interlayer frictional performance of tellurene under controlled twist angles, with the β-Te phase exhibiting superior lubricity over α-Te and the formation of an allotropic homojunction (α-Te/β-Te) further enhancing the interlayer superlubricity. These results not only deepen our understanding of the tribological performance of tellurene but also offer a new perspective on the frictional behavior of 2D materials.

Unraveling the mechanism of carbonic anhydrase IX inhibition by alkaloids from Ruta chalepensis: A synergistic analysis of in vitro and in silico data

Due to the pivotal role of carbonic anhydrase IX (CA IX) in pathological conditions, there's a pressing need for novel inhibitors to improve patient outcomes and clinical management. Herein, we investigated the inhibitory efficacy of six alkaloids from Ruta chalepensis against CA IX through in vitro inhibition assay and computational modeling. Skimmianine and maculosidine displayed significant inhibitory activity in vitro, with low IC50 values of 105.2 ± 3.2 and 295.7 ± 14.1 nM, respectively. Enzyme kinetics analyses revealed that skimmianine exhibited a mixed inhibition mode, contrasting with the noncompetitive inhibition mechanism observed for the reference drug (acetazolamide), as indicated by intersecting lines in the Lineweaver-Burk plots. The findings of docking calculations revealed that skimmianine and maculosidine exhibited extensive polar interactions with the enzyme. These alkaloids demonstrate substantial binding interactions and occupy identical binding site as acetazolamide, thereby enhancing their efficacy as inhibitors of CA IX. Utilizing a 100 ns molecular dynamics (MD) simulation, the dynamic interactions between isolated alkaloids and CA IX were intensively assessed. Analysis of diverse MD parameters revealed that skimmianine and maculosidine displayed consistent trajectories and notable energy stabilization during their interaction with CA IX. The findings of MM/PBSA analysis depicted the minimum binding free energy for skimmianine and maculosidine. In addition, the Potential Energy Landscape (PEL) analysis revealed distinct and stable conformational states for the CA IX-ligand complexes, with Skimmianine showing the most stable and lowest energy configuration. These computational findings align with experimental results, emphasizing the potential efficacy of skimmianine and maculosidine as inhibitors of CA IX.

Ab initio computations from 78Ni towards 70Ca along neutron number N = 50

We present coupled-cluster computations of nuclei with neutron number N=50 “south” of 78Ni using nucleon-nucleon and three-nucleon forces from chiral effective field theory. We find an erosion of the magic number N=50 toward 70Ca manifesting itself by an onset of deformation and increased complexity in the ground states. For 78Ni, we predict a low-lying rotational band consistent with recent data, which up until now has been a challenge for ab initio nuclear models. Ground states are deformed in 76Fe, 74Cr, and 72Ti, although the spherical states are too close in energy to unambiguously identify the shape of the ground state within the uncertainty estimates. In 70Ca, the potential energy landscape from quadrupole-constrained Hartree-Fock computations flattens, and the deformation becomes less rigid. We also compute the low-lying spectra and B(E2) values for these neutron-rich N=50 nuclei.

True Random Number Generator Based on Chaotic Oscillation of a Tunable Double-Well MEMS Resonator.

Chaotic systems have aroused interest across various scientific disciplines such as physics, biology, chemistry, and meteorology. The deterministic but unpredictable nature of a chaotic system is an ideal feature for random number generation. Microelectromechanical systems (MEMS) are a promising technology that effectively harnesses chaos, offering advantages such as a compact footprint, scalability, and low power consumption. This paper presents a true random number generator (TRNG) based on a double-well MEMS resonator integrated with an actuator and position sensor. The potential energy landscape of the proposed MEMS resonator is actively tunable with a direct current voltage. Experimental demonstrations of tunable bistability and chaotic resonance are reported in this paper. A chaotic time sequence is generated through piezoresistive sensing of the position of the MEMS resonator once it is driven into the chaotic regime. Subsequently, the randomness of the bit sequence, achieved by applying the exclusive or function to a digital chaotic sequence and its delayed differential is confirmed to meet the National Institute of Standards and Technology specifications. Moreover, the throughput and energy efficiency of the proposed MEMS-based TRNG can be adjusted from 50 kb s-1 and 0.44 pJ per bit at a low energy barrier to 167 kb s-1 and 6.74 pJ per bit at a high energy barrier by changing the MEMS device's potential well. The tunability of the proposed double-well MEMS resonator not only offers continuous adjustments in the energy efficiency of TNRG but also unveils vast and diverse research opportunities in analog computing, encryption, and secure communications.

A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data.

Learning cellular dynamics through reconstruction of the underlying cellular potential energy landscape (aka Waddington landscape) from time-series single-cell RNA sequencing (scRNA-seq) data is a current challenge. Prevailing data-driven computational methods can be hampered by the lack of physical principles to guide learning from complex data, resulting in reduced prediction accuracy and interpretability when applied to infer cell population dynamics. Here, we propose PI-SDE, a physics-informed neural stochastic differential equation (SDE) framework that combines the Hamilton-Jacobi (HJ) equation and neural SDE to learn cellular dynamics. Grounded in potential energy theory of biological systems, PI-SDE integrates the principle of least action by enforcing the HJ equation when reconstructing cellular potential energy function. This approach not only facilitates accurate predictions, but also improves interpretability, especially in the reconstructed potential energy landscape. Through benchmarking on two real scRNA-seq datasets, we demonstrate the importance of incorporating the HJ regularization term in dynamic inference, especially in predicting gene expression at held-out time points. Meanwhile, the learned potential energy landscape provides biologically interpretable insights into the process of cell differentiation. Our framework enhances model performance, while maintaining robustness and stability. PI-SDE software is available at https://github.com/QiJiang-QJ/PI-SDE.

Computational Characterization of the On-Surface Synthesis of Low-Dimensional Nanostructures

On-surface chemistry represents a fast-growing field allowing to synthesize molecular structures not available by traditional wet chemistry. In this context, high-resolution scanning probe microscopy techniques are able to provide unprecedented spatial resolution, allowing to precisely identify individual products of the reactions. Nevertheless, a deep understanding of the reaction mechanism under the conditions imposed by the substrate remains unknown.Nowadays, various computational strategies are employed to characterize the optimal reaction pathways of the chemical processes that take place on the surfaces of solids. For example, one of the most popular approaches is the nudged elastic band (NEB) as well as different transition state theory methods beyond it. These approaches had demonstrated their capability to characterize the potential energy landscape of the reaction pathways [1-2]. Another approach that had attracted the interest of the on-surface synthesis community is the application of molecular dynamics (MD) methods, within the canonical ensemble, and based on the QM/MM (quantum mechanics/molecular mechanics) of the interatomic forces [3-4]. This approach, which combines Density functional theory (DFT) and empirical force fields to describe the interaction forces, allows to simulate very large systems, at a very low computational cost while still accounting for the crucial quantum mechanics interactions. Also, such temperature-dependent simulations include the effect of entropy, vibrations modes, concerted motion, etc.I will present the experimental and computational results of different projects in which we have made use of the aforementioned computational approaches to achieve a better understanding of the on-surface synthesis experiments performed by the group. For example, the rationalization of the selective cyclodehydrogenation of non-benzenoid nanographenes deposited on a Au(111) substrate by means of temperature-dependent MD calculations and the analysis of the obtained vibrational modes (See Figure 1a). The group had also studied the stepwise transformation of coronene-based organometallic networks into two-dimensional covalent patches through intermolecular homocoupling (See Figure 1b). We had as well investigated the transformation of indenyl precursors, through a triple C-H cleavage followed by a peripheral directed homocoupling, to form a polyacetylene backboned polymer that features a parity dependent and highly delocalized soliton in-gap state (See Figure 1c).

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