Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines

• URL: http://arxiv.org/abs/2205.06313v1
• Date: Thu, 12 May 2022 18:59:43 GMT
• Title: Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines
• Authors: William Poole, Thomas Ouldridge, Manoj Gopalkrishnan, and Erik Winfree
• Abstract summary: We show how a biochemical computer can use intrinsic chemical noise to perform complex computations. We also use our explicit physical model to derive thermodynamic costs of inference.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Can a micron sized sack of interacting molecules understand, and adapt to a constantly-fluctuating environment? Cellular life provides an existence proof in the affirmative, but the principles that allow for life's existence are far from being proven. One challenge in engineering and understanding biochemical computation is the intrinsic noise due to chemical fluctuations. In this paper, we draw insights from machine learning theory, chemical reaction network theory, and statistical physics to show that the broad and biologically relevant class of detailed balanced chemical reaction networks is capable of representing and conditioning complex distributions. These results illustrate how a biochemical computer can use intrinsic chemical noise to perform complex computations. Furthermore, we use our explicit physical model to derive thermodynamic costs of inference.

Related papers

• The Origin and Evolution of Information Handling [0.6963971634605796]
  We explain how information control emerged ab initio and how primitive control mechanisms in life might have evolved, becoming increasingly refined. By describing precisely the primordial transitions in chemistry-based computation, our framework is capable of explaining the above-mentioned gaps. Being compatible with the free energy principle, we have developed a computational enactivist theoretical framework that could be able to describe from the origin of life to high-level cognition.
  arXiv  Detail & Related papers  (2024-04-05T19:35:38Z)
• A Review of Neuroscience-Inspired Machine Learning [58.72729525961739]
  Bio-plausible credit assignment is compatible with practically any learning condition and is energy-efficient. In this paper, we survey several vital algorithms that model bio-plausible rules of credit assignment in artificial neural networks. We conclude by discussing the future challenges that will need to be addressed in order to make such algorithms more useful in practical applications.
  arXiv  Detail & Related papers  (2024-02-16T18:05:09Z)
• AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning [45.379791270351184]
  RMechRP is a new deep learning-based reaction predictor system. We develop and train models using RMechDB, a public database of radical reactions. Our results demonstrate the effectiveness of RMechRP in providing accurate and interpretable predictions.
  arXiv  Detail & Related papers  (2023-11-02T09:47:27Z)
• Autonomous Learning of Generative Models with Chemical Reaction Network Ensembles [0.0]
  We develop a general architecture whereby a broad class of chemical systems can autonomously learn complex distributions. Our construction takes the form of a chemical implementation of machine learning's optimization workhorse: gradient descent on the relative entropy cost function.
  arXiv  Detail & Related papers  (2023-11-02T03:46:23Z)
• 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)
• A linear response framework for simulating bosonic and fermionic correlation functions illustrated on quantum computers [58.720142291102135]
  Lehmann formalism for obtaining response functions in linear response has no direct link to experiment. Within the context of quantum computing, we make the experiment an inextricable part of the quantum simulation. We show that both bosonic and fermionic Green's functions can be obtained, and apply these ideas to the study of a charge-density-wave material.
  arXiv  Detail & Related papers  (2023-02-20T19:01:02Z)
• 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)
• A modular quantum-classical framework for simulating chemical reaction pathways accurately [0.0]
  We present a modular quantum-classical hybrid framework to accurately simulate chemical reaction pathway. We demonstrate our framework by accurately tracing the isomerization pathway for small organic molecules. This framework can now be readily applied to study other 'active' molecules from the pharma and chemical industries.
  arXiv  Detail & Related papers  (2022-10-17T10:41:53Z)
• 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)
• A Quantum Finite Automata Approach to Modeling the Chemical Reactions [0.0]
  The study of chemical information processing with quantum computational models is a natural goal. We have modeled chemical reactions using two-way quantum finite automata, which are halted in linear time. It has been proven that computational versatility can be increased by combining chemical accept/reject signatures and quantum automata models.
  arXiv  Detail & Related papers  (2020-07-08T09:15:33Z)
• Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network [0.0]
  Chemical reactions occur in energy, environmental, biological, and many other natural systems. Here, we present a neural network approach that autonomously discovers reaction pathways from the time-resolved species concentration data. The proposed Chemical Reaction Neural Network (CRNN), by design, satisfies the fundamental physics laws, including the Law of Mass Action and the Arrhenius Law.
  arXiv  Detail & Related papers  (2020-02-20T23:36:46Z)

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.