Multi-level Protocol for Mechanistic Reaction Studies Using Semi-local Fitted Potential Energy Surfaces

• URL: http://arxiv.org/abs/2304.00942v2
• Date: Tue, 25 Jul 2023 08:36:38 GMT
• Title: Multi-level Protocol for Mechanistic Reaction Studies Using Semi-local Fitted Potential Energy Surfaces
• Authors: Tomislav Piskor, Peter Pinski, Thilo Mast, Vladimir V. Rybkin
• Abstract summary: We propose a multi-scale protocol for routine theoretical studies of chemical reaction mechanisms. The key aspect of the method's performance is its multi-scale nature, which not only saves computational effort but also allows extracting meaningful information.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose a multi-scale protocol for routine theoretical studies of chemical reaction mechanisms. The initial reaction paths of our investigated systems are sampled using the Nudged-Elastic Band (NEB) method driven by a cheap electronic structure method. Forces recalculated at the more accurate electronic structure theory for a set of points on the path are fitted with a machine-learning technique (in our case symmetric gradient domain machine learning or sGDML) to produce a semi-local reactive Potential Energy Surface (PES), embracing reactants, products and transition state (TS) regions. This approach has been successfully applied to a unimolecular (Bergman cyclization of enediyne) and a bimolecular (S$_\text{N}$2 substitution) reaction. In particular, we demonstrate that with only 50 to 150 energy-force evaluations with the accurate reference methods (here CASSCF and CCSD) it is possible to construct a semi-local PES giving qualitative agreement for stationary-point geometries, intrinsic reaction-coordinates and barriers. Furthermore, we find a qualitative agreement in vibrational frequencies and reaction rate coefficients. The key aspect of the method's performance is its multi-scale nature, which not only saves computational effort but also allows extracting meaningful information along the reaction path, characterized by zero gradients in all but one direction. Agnostic to the nature of the TS and computationally economic, the protocol can be readily automated and routinely used for mechanistic reaction studies.

Related papers

• A Roadmap for Simulating Chemical Dynamics on a Parametrically Driven Bosonic Quantum Device [32.65699367892846]
  We investigate the feasibility of simulating reaction dynamics using a bosonic superconducting Kerr-cat device. This approach provides control over parameters defining the double-well free energy profile, as well as external factors like temperature and the coupling strength between the reaction coordinate and the thermal bath of non-reactive degrees of freedom.
  arXiv  Detail & Related papers  (2024-09-19T22:43:08Z)
• ReactAIvate: A Deep Learning Approach to Predicting Reaction Mechanisms and Unmasking Reactivity Hotspots [4.362338454684645]
  We develop an interpretable attention-based GNN that achieved near-unity and 96% accuracy for reaction step classification. Our model adeptly identifies key atom(s) even from out-of-distribution classes. This generalizabilty allows for the inclusion of new reaction types in a modular fashion, thus will be of value to experts for understanding the reactivity of new molecules.
  arXiv  Detail & Related papers  (2024-07-14T05:53:18Z)
• Force-free identification of minimum-energy pathways and transition states for stochastic electronic structure theories [0.18641315013048293]
  We present strategies that utilize the surrogate Hessian line-search method to efficiently identify MEP and TS structures. We show that it is possible to identify MEPs and TSs using a force-free Quantum Monte Carlo approach.
  arXiv  Detail & Related papers  (2024-02-20T17:58:43Z)
• Generative Model for Constructing Reaction Path from Initial to Final States [0.47477099173857373]
  This paper presents an innovative approach that utilizes neural networks to generate initial guesses for reaction pathways. The proposed method is initiated by inputting the coordinates of the initial state, followed by progressive alterations to its structure. The application of this geometry-based method extends to complex reaction pathways illustrated by organic reactions.
  arXiv  Detail & Related papers  (2024-01-19T14:32:50Z)
• Quantum Eigenvector Continuation for Chemistry Applications [57.70351255180495]
  We show that a significant amount of (quantum) computational effort can be saved by making use of already calculated ground states. In all cases, we show that the PES can be captured using relatively few basis states.
  arXiv  Detail & Related papers  (2023-04-28T19:22:58Z)
• Differentiable Programming of Chemical Reaction Networks [63.948465205530916]
  Chemical reaction networks are one of the most fundamental computational substrates used by nature. We study well-mixed single-chamber systems, as well as systems with multiple chambers separated by membranes. We demonstrate that differentiable optimisation, combined with proper regularisation, can discover non-trivial sparse reaction networks.
  arXiv  Detail & Related papers  (2023-02-06T11:41:14Z)
• Local manifold learning and its link to domain-based physics knowledge [53.15471241298841]
  In many reacting flow systems, the thermo-chemical state-space is assumed to evolve close to a low-dimensional manifold (LDM) We show that PCA applied in local clusters of data (local PCA) is capable of detecting the intrinsic parameterization of the thermo-chemical state-space.
  arXiv  Detail & Related papers  (2022-07-01T09:06:25Z)
• Real-time equation-of-motion CC cumulant and CC Green's function simulations of photoemission spectra of water and water dimer [54.44073730234714]
  We discuss results obtained with the real-time equation-of-motion CC cumulant approach. We compare the ionization potentials obtained with these methods for the valence region. We analyze unique features of the spectral functions, associated with the position of satellite peaks, obtained with the RT-EOM-CC and CCGF methods.
  arXiv  Detail & Related papers  (2022-05-27T18:16:30Z)
• Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
  We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves. We find both methods provide good estimates of the energy and ground state. gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
  arXiv  Detail & Related papers  (2020-11-02T19:52:04Z)
• Towards the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space [0.0]
  We introduce a reactant-to-barrier (R2B) machine learning model that rapidly and accurately infers activation energies and transition state geometries. R2B enjoys improving accuracy as training sets grow, and requires as input solely molecular graph information of the reactant. We provide numerical evidence for the applicability of R2B for two competing text-book reactions relevant to organic synthesis, E2 and SN2.
  arXiv  Detail & Related papers  (2020-09-28T15:50:32Z)
• Simulation of Thermal Relaxation in Spin Chemistry Systems on a Quantum Computer Using Inherent Qubit Decoherence [53.20999552522241]
  We seek to take advantage of qubit decoherence as a resource in simulating the behavior of real world quantum systems. We present three methods for implementing the thermal relaxation. We find excellent agreement between our results, experimental data, and the theoretical prediction.
  arXiv  Detail & Related papers  (2020-01-03T11:48:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.