Neural network ensemble for computing cross sections for rotational transitions in H$_{2}$O + H$_{2}$O collisions

• URL: http://arxiv.org/abs/2507.18974v1
• Date: Fri, 25 Jul 2025 05:59:32 GMT
• Title: Neural network ensemble for computing cross sections for rotational transitions in H$_{2}$O + H$_{2}$O collisions
• Authors: Bikramaditya Mandal, Dmitri Babikov, Phillip C. Stancil, Robert C. Forrey, Roman V. Krems, Naduvalath Balakrishnan,
• Abstract summary: We present a machine learning tool using an ensemble of neural networks (NNs) to predict cross sections.<n>The proposed methodology utilizes data computed with a mixed quantum-classical theory (MQCT)<n>Using only about 10% of the computed data for training, the NNs predict cross sections of state-to-state rotational transitions of H$_2$O + H$_2$O collision.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Water (H$_2$O) is one of the most abundant molecules in the universe and is found in a wide variety of astrophysical environments. Rotational transitions in H$_2$O + H$_2$O collisions are important in modeling environments rich in water molecules but they are computationally intractable using quantum mechanical methods. Here, we present a machine learning (ML) tool using an ensemble of neural networks (NNs) to predict cross sections to construct a database of rate coefficients for rotationally inelastic transitions in collisions of complex molecules such as water. The proposed methodology utilizes data computed with a mixed quantum-classical theory (MQCT). We illustrate that efficient ML models using NN can be built to accurately interpolate in the space of 12 quantum numbers for rotational transitions in two asymmetric top molecules, spanning both initial and final states. We examine various architectures of data corresponding to each collision energy, symmetry of water molecule, and excitation/de-excitation rotational transitions, and optimize the training/validation data sets. Using only about 10\% of the computed data for training, the NNs predict cross sections of state-to-state rotational transitions of H$_{2}$O + H$_{2}$O collision with average relative root mean square error of 0.409. Thermally averaged cross sections, computed using the predicted state-to-state cross sections ($\sim$90\%) and the data used for training and validation ($\sim$10\%) were compared against those obtained entirely from MQCT calculations. The agreement is found to be excellent with an average percent deviation of about $\sim$13.5\%. The methodology is robust, and thus, applicable to other complex molecular systems.

Related papers

• Accurate generation of chemical reaction transition states by conditional flow matching [0.6872939325656702]
  We introduce TS-GEN, a conditional flow-matching generative model.<n>It maps samples from a simple Gaussian prior directly to transition-state saddle-point geometries in a single, deterministic pass.<n>It delivers unprecedented accuracy, achieving a root-mean-square deviation of $0.004 rmmathringA$.
  arXiv  Detail & Related papers  (2025-07-14T17:54:47Z)
• Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations [63.945006006152035]
  tensor decomposition networks (TDNs) achieve competitive performance with dramatic speedup in computations.<n>We evaluate TDNs on PubChemQCR, a newly curated molecular relaxation dataset containing 105 million DFT-calculated snapshots.
  arXiv  Detail & Related papers  (2025-07-01T18:46:27Z)
• Rate Coefficients for Rotational State-to-State Transitions in H$_2$O + H$_2$ Collisions as Predicted by Mixed Quantum/Classical Theory (MQCT) [0.0]
  A new database of collisional rate coefficients for transitions between the rotational states of H$$O collided with H$$ background gas is developed. The goal is to expand over the other existing databases in terms of the rotational states of water (200 states are included here) and the rotational states of hydrogen (10 states)
  arXiv  Detail & Related papers  (2024-08-23T19:19:53Z)
• Dynamic, Symmetry-Preserving, and Hardware-Adaptable Circuits for Quantum Computing Many-Body States and Correlators of the Anderson Impurity Model [0.0]
  Hamiltonian expectation values are shown to require $omega(N_q) N_textmeas. leq O(N_textimpN_textbath)$ symmetry-preserving, parallel measurement circuits. Our ansatz provides a useful tool to account for electronic correlations on early fault-tolerant processors.
  arXiv  Detail & Related papers  (2024-05-23T21:41:28Z)
• Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling [51.83761266429285]
  We propose a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them. Moleformer achieves state-of-the-art on the initial state to relaxed energy prediction of OC20 and is very competitive in QM9 on predicting quantum chemical properties.
  arXiv  Detail & Related papers  (2023-02-02T03:49:57Z)
• Graph neural networks for fast electron density estimation of molecules, liquids, and solids [0.0]
  We present a machine learning framework for the prediction of $rho(vecr)$. The model is tested across multiple data sets of molecules (QM9), liquid ethylene carbonate electrolyte (EC) and LixNiyMnzCo (1-y-z)O2 lithium ion battery cathodes (NMC)
  arXiv  Detail & Related papers  (2021-12-01T16:57:31Z)
• Computing molecular excited states on a D-Wave quantum annealer [52.5289706853773]
  We demonstrate the use of a D-Wave quantum annealer for the calculation of excited electronic states of molecular systems. These simulations play an important role in a number of areas, such as photovoltaics, semiconductor technology and nanoscience.
  arXiv  Detail & Related papers  (2021-07-01T01:02:17Z)
• Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning [59.71676469100807]
  This work sharpens the sample complexity of synchronous Q-learning to an order of $frac|mathcalS|| (1-gamma)4varepsilon2$ for any $0varepsilon 1$. Our finding unveils the effectiveness of vanilla Q-learning, which matches that of speedy Q-learning without requiring extra computation and storage.
  arXiv  Detail & Related papers  (2021-02-12T14:22:05Z)
• Even more efficient quantum computations of chemistry through tensor hypercontraction [0.6234350105794442]
  We describe quantum circuits with only $widetildecal O(N)$ Toffoli complexity that block encode the spectra of quantum chemistry Hamiltonians in a basis of $N$ arbitrary orbitals. This is the lowest complexity that has been shown for quantum computations of chemistry within an arbitrary basis.
  arXiv  Detail & Related papers  (2020-11-06T18:03:29Z)
• Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction [63.41789556777387]
  Asynchronous Q-learning aims to learn the optimal action-value function (or Q-function) of a Markov decision process (MDP) We show that the number of samples needed to yield an entrywise $varepsilon$-accurate estimate of the Q-function is at most on the order of $frac1mu_min (1-gamma)5varepsilon2+ fract_mixmu_min (1-gamma)$ up to some logarithmic factor.
  arXiv  Detail & Related papers  (2020-06-04T17:51:00Z)
• Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
  We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings. In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$. We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
  arXiv  Detail & Related papers  (2020-02-25T19:18:36Z)

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.