Potential Energy Surface Research Articles

Confined and spontaneously transformed oxidation structures due to the intrinsic heterogeneous surface morphology of C3N monolayer.

Identifying the oxidation structure of two-dimensional interfaces is crucial to improve surface chemistry and electronic properties. Beyond graphene with only phenyl rings, a novel carbon-nitrogen material, C3N, presents an intrinsic heterogeneous surface morphology where each phenyl ring is encircled by six nitrogen atoms, yet its atomistic oxidation structure remains unclear. Here, combining a series of density functional theory calculations and abinitio molecular dynamics simulations, we demonstrate that thermodynamically favorable oxidation loci are confined to the phenyl ring, and kinetic transformations of oxidation structures are feasible along the phenyl ring, whereas those toward nitrogen atoms are proven to be extremely difficult. These results are attributed to the lower barrier of oxygen atom migration along the phenyl ring, while the significantly high barriers toward nitrogen atoms are due to the heterogeneous potential energy surface for oxygen-C3N interaction. This work highlights the significance of surface morphology on the characteristics of oxidation structure, offering insights into tunable electronic properties via confined interfacial oxidation.

Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for H3O2.

The singly hydrated hydroxide anion OH-(H2O) is of central importance to a detailed molecular understanding of water; therefore, there is strong motivation to develop a highly accurate potential to describe this anion. While this is a small molecule, it is necessary to have an extensive data set of energies and, if possible, forces to span several important stationary points. Here, we assess two machine-learned potentials, one using the symmetric gradient domain machine learning (sGDML) method and one based on permutationally invariant polynomials (PIPs). These are successors to a PIP potential energy surface (PES) reported in 2004. We describe the details of both fitting methods and then compare the two PESs with respect to precision, properties, and speed of evaluation. While the precision of the potentials is similar, the PIP PES is much faster to evaluate for energies and energies plus gradient than the sGDML one. Diffusion Monte Carlo calculations of the ground vibrational state, using both potentials, produce similar large anharmonic downshift of the zero-point energy compared to the harmonic approximation of the PIP and sGDML potentials. The computational time for these calculations using the sGDML PES is roughly 300 times greater than using the PIP one.

Density Functional Theory Study on the Mechanism of Nickel-Catalyzed 3,3-Dialkynylation of 2-Aryl Acrylamides Via Double Vinylic C-H Bond Activation.

The mechanisms of Ni-catalyzed 3,3-dialkynylation of 2-aryl acrylamide have been investigated by using density functional theory calculations. The result shows that this reaction includes double alkynylation, which involves sequential key steps of vinylic C-H bond activation, successive oxidative addition, and reductive elimination, with the second C-H bond activation being the rate-determining step. C-H and N-H bond activation occurs via the concerted metalation-deprotonation mechanism. The calculations show that no transition state exists in the first reductive elimination process, and a negative free energy barrier in the second reductive elimination process though a transition state is identified, indicating that the nickel-catalyzed vinylic C(sp2)-C(sp) bond formation does not require activation energy. Z-E isomerization is the prerequisite for the second alkynylation. In addition, our spin-flip TDDFT (SF-TDDFT) computational result discloses that the actual process of Z-E isomerization occurs on the potential energy surface of the first excited singlet state S1.

Inhibition of Vanadium Cathode Dissolution in Zinc‐Ion Batteries on Thermodynamics and Kinetics by Guest Pre‐Intercalation

AbstractAqueous zinc‐ion batteries (AZIBs), recognized for their safety and environmental friendliness, hold significant promise for large‐scale energy storage. However, the rapid capacity degradation resulting from the dissolution of active cathode materials hampers the advancement of AZIBs. Here, Ru0.2V2O5∙0.41H2O (RuVO) is synthesized with remarkable capacity retention (98.2% over 5000 cycles at 10 A g−1). The pre‐intercalation of Ru3+ enhances the stability of both intrinsic and cycling structures, elevating the Gibbs free energy and suppressing V‐dissolution thermodynamically. Additionally, Ru3+ intercalation modulates the potential energy surface of Zn2+ migration, leading to the dominance of Zn2+ in the insertion/extraction mechanism, thereby kinetically impeding the dissolution reaction. This study elucidates the dissolution thermodynamics and kinetics of V‐based cathodes through a combination of experiments, mechanism analyses, and density functional theory (DFT) calculations.

Unified deep learning network for enhanced accuracy in predicting thermal conductivity of bilayer graphene, hexagonal boron nitride, and their heterostructures

In this research, we utilized density functional theory (DFT) computations to perform ab initio molecular dynamics simulations and static calculations on graphene, hexagonal boron nitride, and their heterostructures, subjecting them to strains, perturbations, twist angles, and defects. The gathered energy, force, and virial information informed the creation of a training set comprising 1253 structures. Employing the Neural Evolutionary Potential framework integrated into Graphics Processing Units Molecular Dynamics, we fitted a machine learning potential (MLP) that closely mirrored the DFT potential energy surface. Rigorous validation of lattice constants and phonon dispersion relations confirmed the precision and dependability of the MLP, establishing a solid foundation for subsequent thermal transport investigations. A further analysis of the impact of twist angles uncovered a significant reduction in thermal conductivity, particularly notable in heterostructures with a decline exceeding 35%. The reduction in thermal conductivity primarily stems from the twist angle-induced softening of phonon modes and the accompanying increase in phonon scattering rates, which intensifies anharmonic interactions among phonons. Our study underscores the efficacy of the MLP in delineating the thermal transport attributes of two-dimensional materials and their heterostructures, while also elucidating the micro-mechanisms behind the influence of the twist angle on thermal conductivity, offering fresh perspectives for the design of advanced thermal management materials.

Calculating Potential Energy Surfaces with Quantum Computers by Measuring Only the Density Along Adiabatic Transitions.

We show that chemically accurate potential energy surfaces (PESs) can be generated from quantum computers by measuring only the density along an adiabatic transition between different molecular geometries. In lieu of using phase estimation, the energy is evaluated by performing line-integration using the inverted real-space time-dependent density functional theory Kohn-Sham (KS) potential obtained from the geometry-varying densities of the full wave function. The accuracy of this method depends on the validity of the adiabatic evolution itself and the potential inversion process (which is theoretically exact but can be numerically unstable), whereas the total evolution time is the defining factor for the precision of phase estimation. We examine the method with a one-dimensional system of two electrons for both the ground and first triplet states in first quantization, as well as the ground state of three- and four-electron systems in second quantization. It is shown that few accurate measurements can be utilized to obtain chemical accuracy across the full potential energy curve, with a shorter propagation time than may be required using phase estimation for a similar accuracy. We also show that an accurate potential energy curve can be calculated by making many imprecise density measurements (using a few shots) along the time evolution and smoothing the resulting density evolution. Finally, it is important to note that the method is able to classically provide a check of its own accuracy by comparing the density resulting from a time-independent KS calculation using the inverted potential with the measured density. This can be used to determine whether longer adiabatic evolution times are required to satisfy the adiabatic theorem.

Dissociation Constant of Di‐ and Tri‐Basic Solvents Based on Piperazine and Its Derivatives for Post‐Combustion CO2 Capture

AbstractAmine solvents remain popular in the industries for post combustion carbon dioxide (CO2) capture. Dissociation constant of basic amine solvents plays significant role to influence the CO2 absorption. It is thus important to study the dissociation of new amine solvents for assessing their viability for CO2 capture. In the present work, potentiometric titration method is employed to determine the dissociation constants of dibasic (e. g., Piperazine (PZ), 1‐methyl piperazine (1‐MPZ) and tribasic amines (1‐(2‐aminoethyl‐piperazine) (AEPZ) which could be promising for formulating blended solvent system towards enhancing the rate of CO2 absorption. The estimated dissociation constants for the PZ and 1‐MPZ while match well with reported results, the dissociation constants for AEPZ has been determined newly here within the temperature range 298—318 K. In addition, the structural geometric parameters and Natural Bond Order (NBO) calculations of the amine solvents are performed based on Density Functional Theory (DFT). The NBO calculations predict charge distribution on the nitrogen and other elements, and envisage its effect on the pKa values of the amines. Further, potential energy surface (PES) scanning calculations are performed for AEPZ to explain it's ring inversion which is responsible for obtaining a single pKa value in experimental studies.

OH-Formation following vibrationally induced reaction dynamics of H2COO.

The reaction dynamics of H2COO to form HCOOH and dioxirane as first steps for OH-elimination is quantitatively investigated. Using a machine learned potential energy surface (PES) at the CASPT2/aug-cc-pVTZ level of theory vibrational excitation along the CH-normal mode νCH with energies up to 40.0 kcal mol-1 (∼5νCH) leads almost exclusively to HCOOH which further decomposes into OH + HCO. Although the barrier to form dioxirane is only 21.4 kcal mol-1 the reaction probability to form dioxirane is two orders of magnitude lower if the CH-stretch mode is excited. Following the dioxirane-formation pathway is facile, however, if the COO-bend vibration is excited together with energies equivalent to ∼2νCH or ∼3νCOO. For OH-formation in the atmosphere the pathway through HCOOH is probably most relevant because the alternative pathways (through dioxirane or formic acid) involve several intermediates that can de-excite through collisions, relax via internal vibrational relaxation (IVR), or pass through loose and vulnerable transition states (formic acid). This work demonstrates how, by selectively exciting particular vibrational modes, it is possible to dial into desired reaction channels with a high degree of specificity.

Tuning the Oxygen Reduction Reactivity of Layered Perovskites Using the Jahn-Teller Effect.

Compositional tuning of layered perovskite oxides provides a means of systematically studying how local distortions affect fundamental aspects of electrochemical reaction pathways. Structural analysis of a family of samples La1.2Sr0.8Ni1-yCoyO4 shows that Ni-rich compositions have an expanded crystalline c axis, which is anisotropically compressed by systematic Co incorporation. Raman spectra reveal the strong growth of a symmetry forbidden mode, which suggests that Co acts through localized distortions. Crystallographic and spectroscopic parameters describing this structural distortion correlate to the measured Tafel slopes for the oxygen reduction reaction for all Ni-containing samples, which is attributed to the distortion of potential energy surfaces by the Jahn-Teller expansion of d7 Ni(III) cations. Incorporation of Co not only minimizes the distortion but alters the apparent selectivity of the oxygen reduction reaction away from H2O2 and toward H2O. Rotating ring-disk electrochemical measurements, however, indicate that the apparent change in selectivity is due to activation of a first-order chemical disproportionation of H2O2 that is activated by Co in the lattice. These outcomes will support efforts to design electrocatalysts and reactors for the electrochemical synthesis of H2O2.

Applications of quantum circuit learning model using particle-number-conserving state on quantum chemical calculations

Although the variational quantum eigensolver is a typical quantum algorithm utilized in near-term quantum devices, many measurements are required in an iterative closed feedback loop between the classical and quantum computers to obtain sufficient accuracy. In this study, we attempt to construct a quantum circuit learning model to infer potential energy surfaces and atomic forces without using the iterative loop to optimize parameters for every bond length. To realize a high, accurate inference performance, measurement is introduced in the middle of the circuit. When the proposed quantum circuit learning model is applied to the H2 molecule, the energies and atomic forces can be estimated with high accuracy in a single feed-forward calculation with varying bond lengths. A nonlinear relation between outcomes from data-encoding layer and inputs can also be used to eliminate the data-encoding layer, allowing for our quantum circuit model with lower learning costs by reducing the number of parameters to be optimized. Our model can be also extended to six-qubit systems, such as the H3 molecule, and to the water molecule with two internal degrees of freedom. Finally, we use the IBM Quantum backend to perform inference with a real quantum computer and show the effect of noise on the actual quantum device.

Multiple-time scale integration method based on an interpolated potential energy surface for abinitio path integral molecular dynamics.

A new multiple-time scale integration method is presented that propagates abinitio path integral molecular dynamics (PIMD). This method uses a large time step to generate an approximate geometrical configuration whose energy and gradient are evaluated at the level of an abinitio method, and then, a more precise integration scheme, e.g., the Bulirsch-Stoer method or velocity Verlet integration with a smaller time step, is used to integrate from the previous step using the computationally efficient interpolated potential energy surface constructed from two consecutive points. This method makes the integration of PIMD more efficient and accurate compared with the velocity Verlet integration. A Nosé-Hoover chain thermostat combined with this new multiple-time scale method has good energy conservation even with a large time step, which is usually challenging in velocity Verlet integration for PIMD due to the very small chain mass when a large number of beads are used. The new method is used to calculate infrared spectra and free energy profiles to demonstrate its accuracy and capabilities.

Generalized Born-Huang expansion under macroscopic quantum electrodynamics framework.

Born-Huang expansion is the cornerstone for studying potential energy surfaces and non-adiabatic couplings (NACs) in molecular systems. However, the traditional approach is insufficient to describe the molecular system, which strongly interacts with quantum light. Inspired by the work by Schäfer et al., we develop the generalized Born-Huang expansion theory within a macroscopic quantum electrodynamics (QED) framework. The theory we present allows us to describe electromagnetic vacuum fluctuations in dielectric media and incorporate the effects of dressed photons (or polaritons) into NACs. With the help of the generalized Born-Huang expansion, we clearly classify electronic nuclear NACs, polaritonic nuclear NACs, and polaritonic electronic NACs. Furthermore, to demonstrate the advantage of the macroscopic QED framework, we estimate polaritonic electronic NACs without any free parameter, such as the effective mode volume, and demonstrate the distance dependence of the polaritonic electronic NACs in a silver planar system. In addition, we take a hydrogen atom in free space as an example and derive spontaneous emission rates from photonic electronic NACs (polaritonic electronic NACs are reduced to photonic electronic NACs). We believe that this work not only provides an avenue for the theoretical exploration of NACs in a nucleus-electron-polariton coupled system but also offers a more comprehensive understanding for molecules coupled with quantum light.

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals.

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

Stabilization of Charge-Transfer Excited States in Biological Systems: A Computational Focus on the Special Pair in Photosystem II Reaction Centers.

Charge-transfer (CT) excited states play an important role in many biological processes. However, many computational approaches often inadequately address the equilibration effects of nuclear and environmental degrees of freedom on these states. One prominent example of systems in which CT states are of utmost importance is reaction centers (RC) in photosystems. Here we use a multiscale approach combined with time-dependent density functional theory to explore the lowest CT excited state of the special pair PD1-PD2 in the Photosystem II-RC of a cyanobacterium. We find that the nonequilibrium CT excited state resides near the Soret band, making an exciton the lowest-energy excited state. However, accounting for nuclear and state-specific dielectric equilibration along the CT potential energy surface (PES), the CT state PD1--PD2+ stabilizes energetically below the excitonic state. This underscores the crucial role of state-specific solvation in mapping the PES of CT states, as demonstrated in a simplified dimer model.

Abundance of Low-Energy Oxygen Vacancy Pairs Dictates the Catalytic Performance of Cerium-Stabilized Zirconia.

Cerium-stabilized zirconia (Ce1-xZrxOy, CZO) is renowned for its superior oxygen storage capacity (OSC), a key property long believed to be beneficial to catalytic oxidation reactions. However, 50% Ce-containing CZO recorded with the highest OSC has disappointingly poor performance in catalytic oxidation reactions compared to those with higher Ce contents but lower OSC ability. Here, we employ global neural network (G-NN)-based potential energy surface exploration methods to establish the first ternary phase diagram for bulk structures of CZO, which identifies three critical compositions of CZO, namely, 50, 60, and 80% Ce-containing CZO that are thermodynamically stable under typical synthetic conditions. 50% Ce-containing CZO, although having the highest OSC, exhibits the lowest O vacancy (Ov) diffusion rate. By contrast, 60% Ce-containing CZO, despite lower OSC (33.3% OSC compared to that of 50% Ce-containing CZO), reaches the highest Ov diffusion ability and thus offers the highest CO oxidation catalytic performance. The physical origin of the high performance of 60% Ce-containing CZO is the abundance of energetically favorable Ov pairs along the ⟨110⟩ direction, which reduces the energy barrier of Ov diffusion in the bulk and promotes O2 activation on the surface. Our results clarify the long-standing puzzles on CZO and point out that 60% Ce-containing CZO is the most desirable composition for typical CZO applications.

Product State-Resolved Reactive Scattering Studies of the H + Cl2 (v0 = 1-3, j0 = 0) → HCl + Cl Reaction by the Time-Dependent Wave Packet Method.

The typical hydrogen atom plus halogen molecule reaction H + Cl2 → HCl + Cl has implications across many fields. In this paper, product state-resolved quantum dynamics calculations for the vibrationally excited reaction H + Cl2 (v0 = 1-3, j0 = 0) → HCl + Cl were conducted using the time-dependent wave packet method on a newly developed accurate potential energy surface. Numerical results indicate that the initial vibrational excitation of Cl2 does enhance the reactivity for this early barrier reaction, although less than the enhancement of the translational energy. The calculated product vibrational state-resolved integral cross sections and rate constants reveal that the product vibrational state distribution and the initial vibrational state of Cl2 are highly correlated. The thermal rate constant in the temperature range from 100 to 1000 K was given and is found to be in reasonable agreement with the experimental measurements.

The future of computational catalysis

The future of computational heterogeneous catalysis is shaped by machine learning in two different but equally important areas: (i) development of atomistic potentials that closely approximate DFT and wavefunction based ab initio methods (MP2, CCSD(T)), but are computationally more efficient, and (ii) finding structure and reactivity descriptors for predicting catalytic materials and reactions.Machine Learning Potentials will enable improved sampling of the potential energy surface (PES) for reaction conditions, but they will not do better than the data on which they are trained. Therefore, this perspective focusses on ways of improving the quality of the PES beyond the generalized gradient approximation (GGA) climbing the Jacob’s ladder of functionals in DFT up to RPA and using wavefunction methods such as MP2 and CCSD(T).It is problem solving in close collaboration with experiment that has made computational methodology relevant and this remains an important aspect of data science methods in heterogeneous catalysis.

A Many-Body Perspective of Nuclear Quantum Effects in Aqueous Clusters.

Nuclear quantum effects play an important role in the structure and thermodynamics of aqueous systems. By performing a many-body expansion with nuclear-electronic orbital (NEO) theory, we show that proton quantization can give rise to significant energetic contributions for many-body interactions spanning several molecules in single-point energy calculations of water clusters. Although zero-point motion produces a large increase in energy at the one-body level, nuclear quantum effects serve to stabilize higher-order molecular interactions. These results are significant because they demonstrate that nuclear quantum effects play a nontrivial role in many-body interactions of aqueous systems. Our approach also provides a pathway for incorporating nuclear quantum effects into water potential energy surfaces. The NEO approach is advantageous for many-body expansion analyses because it includes nuclear quantum effects directly in the energies.

Classical and quantum light-induced non-adiabaticity in molecular systems

The exchange of energy between electronic and nuclear motion is the origin of non-adiabaticity and plays an important role in many molecular phenomena and processes. Conical intersections (CIs) of different electronic potential energy surfaces lead to the most singular non-adiabaticity and have been intensely investigated. The coupling of light and matter induces conical intersections, which are termed light-induced conical intersections (LICIs). There are two kinds of LICIs, those induced by classical (laser) light and those by quantum light like that provided by a cavity. The present work reviews the subject of LICIs, discussing the achievements made so far. Particular attention is paid to comparing classical and quantum LICIs, their similarities and differences and their relationship to naturally occurring CIs. In contrast to natural CIs, the properties of which are dictated by nature, the properties of their light-induced counterparts are controllable by choosing the frequency and intensity (or coupling to the cavity) of the external light source. This opens the door to inducing and manipulating various kinds of non-adiabatic effects. Several examples of diatomic and polyatomic molecules are presented covering both dynamics and spectroscopy. The computational methods employed are discussed as well. To our opinion, the young field of LICIs and their impact shows much future potential.

Fragmentation and charge transfer in cyclic dipeptides with an aromatic side chain induced by VUV radiation

Abstract The fragmentation of three cyclic dipeptides (c-Glycil-Phenylalanine, c-Tryptophan-Tyrosine and c-Tryptophan-Tryptophan), characterized by an aromatic side chain, has been investigated by synchrotron radiation and photoelectron-photoion coincidence (PEPICO) experiments, assisted by atomistic simulations. The PEPICO experiments show that the charged moiety containing the aromatic side chain is the main fragment in the three samples. The theoretical exploration of the potential energy surfaces has allowed to identify the possible fragmentation paths leading to the formation of these fragments. Then, the analysis of the differences in the electronic density distributions of the neutral molecule and the cation and a molecular dynamics simulation provided an understanding of the preferred localization of the positive charge on the aromatic side chain of the cyclic dipeptide.