Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy

• URL: http://arxiv.org/abs/2401.16487v2
• Date: Tue, 16 Apr 2024 15:28:35 GMT
• Title: Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy
• Authors: Ana Molina-Taborda, Pilar Cossio, Olga Lopez-Acevedo, Marylou Gabrié,
• Abstract summary: We develop an approach combining enhanced sampling with deep generative models and active learning of a machine learning potential. We apply this method to study the isomerization of an ultrasmall silver nanocluster, belonging to a set of systems with diverse applications in the fields of medicine and biology.
• Score: 1.7633275579210346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Extracting consistent statistics between relevant free-energy minima of a molecular system is essential for physics, chemistry and biology. Molecular dynamics (MD) simulations can aid in this task but are computationally expensive, especially for systems that require quantum accuracy. To overcome this challenge, we develop an approach combining enhanced sampling with deep generative models and active learning of a machine learning potential (MLP). We introduce an adaptive Markov chain Monte Carlo framework that enables the training of one Normalizing Flow (NF) and one MLP per state, achieving rapid convergence towards the Boltzmann distribution. Leveraging the trained NF and MLP models, we compute thermodynamic observables such as free-energy differences or optical spectra. We apply this method to study the isomerization of an ultrasmall silver nanocluster, belonging to a set of systems with diverse applications in the fields of medicine and catalysis.

Related papers

• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning [60.02391969049972]
  We introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system.
  arXiv  Detail & Related papers  (2023-06-08T17:12:08Z)
• Quantum Car-Parrinello Molecular Dynamics: A Cost-Efficient Molecular Simulation Method on Near-Term Quantum Computers [0.0]
  We propose a cost-reduced method for finite-temperature molecular dynamics on a near-term quantum computer, Quantum Car-Parrinello molecular dynamics (QCPMD) Our method achieves a substantial cost reduction compared with molecular dynamics using the variational quantum eigensolver (VQE)
  arXiv  Detail & Related papers  (2022-12-22T17:59:31Z)
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive [52.77024349608834]
  We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE) This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
  arXiv  Detail & Related papers  (2022-12-21T23:15:17Z)
• Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations [51.68332623405432]
  Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations.
  arXiv  Detail & Related papers  (2022-05-17T13:08:28Z)
• NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic [38.50309739333058]
  We introduce an optimized implementation of the hybrid method (NNP/MM), which combines neural network potentials (NNP) and molecular mechanics (MM) This approach models a portion of the system, such as a small molecule, using NNP while employing MM for the remaining system to boost efficiency. It has enabled us to increase the simulation speed by 5 times and achieve a combined sampling of one microsecond for each complex, marking the longest simulations ever reported for this class of simulation.
  arXiv  Detail & Related papers  (2022-01-20T10:57:20Z)
• Machine learning for excitation energy transfer dynamics [0.0]
  We use the hierarchical equations of motion (HEOM) to simulate open quantum dynamics in the biological regime. We generate a set of time dependent observables that depict the coherent propagation of electronic excitations through the light harvesting complexes. We demonstrate the capability of convolutional neural networks to tackle this research problem.
  arXiv  Detail & Related papers  (2021-12-22T14:11:30Z)
• Quantum Markov Chain Monte Carlo with Digital Dissipative Dynamics on Quantum Computers [52.77024349608834]
  We develop a digital quantum algorithm that simulates interaction with an environment using a small number of ancilla qubits. We evaluate the algorithm by simulating thermal states of the transverse Ising model.
  arXiv  Detail & Related papers  (2021-03-04T18:21:00Z)
• Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics [4.276697874428501]
  We present a novel framework to advance simulation by up to three orders of magnitude. LED learns the effective dynamics of molecular systems. We demonstrate the effectiveness of LED in the M"ueller-Brown potential, the Trp Cage protein, and the alanine dipeptide.
  arXiv  Detail & Related papers  (2021-02-17T15:15:37Z)
• QuTiP-BoFiN: A bosonic and fermionic numerical hierarchical-equations-of-motion library with applications in light-harvesting, quantum control, and single-molecule electronics [51.15339237964982]
  "hierarchical equations of motion" (HEOM) is a powerful exact numerical approach to solve the dynamics. It has been extended and applied to problems in solid-state physics, optics, single-molecule electronics, and biological physics. We present a numerical library in Python, integrated with the powerful QuTiP platform, which implements the HEOM for both bosonic and fermionic environments.
  arXiv  Detail & Related papers  (2020-10-21T07:54:56Z)
• Microcanonical and finite temperature ab initio molecular dynamics simulations on quantum computers [0.0]
  Ab initio molecular dynamics (AIMD) is a powerful tool to predict properties of molecular and condensed matter systems. We provide solutions for the alleviation of the statistical noise associated to the measurements of the expectation values of energies and forces. We also propose a Langevin dynamics algorithm for the simulation of canonical, i.e., constant temperature, dynamics.
  arXiv  Detail & Related papers  (2020-08-18T20:24:27Z)

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.