Bayesian optimization with known experimental and design constraints for chemistry applications

• URL: http://arxiv.org/abs/2203.17241v1
• Date: Tue, 29 Mar 2022 22:16:54 GMT
• Title: Bayesian optimization with known experimental and design constraints for chemistry applications
• Authors: Riley J. Hickman, Matteo Aldeghi, Florian H\"ase, Al\'an Aspuru-Guzik
• Abstract summary: We extend our experiment planning algorithms Phoenics and Gryffin such that they can handle arbitrary known constraints. We illustrate their practical utility in two simulated chemical research scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optimization strategies driven by machine learning, such as Bayesian optimization, are being explored across experimental sciences as an efficient alternative to traditional design of experiment. When combined with automated laboratory hardware and high-performance computing, these strategies enable next-generation platforms for autonomous experimentation. However, the practical application of these approaches is hampered by a lack of flexible software and algorithms tailored to the unique requirements of chemical research. One such aspect is the pervasive presence of constraints in the experimental conditions when optimizing chemical processes or protocols, and in the chemical space that is accessible when designing functional molecules or materials. Although many of these constraints are known a priori, they can be interdependent, non-linear, and result in non-compact optimization domains. In this work, we extend our experiment planning algorithms Phoenics and Gryffin such that they can handle arbitrary known constraints via an intuitive and flexible interface. We benchmark these extended algorithms on continuous and discrete test functions with a diverse set of constraints, demonstrating their flexibility and robustness. In addition, we illustrate their practical utility in two simulated chemical research scenarios: the optimization of the synthesis of o-xylenyl Buckminsterfullerene adducts under constrained flow conditions, and the design of redox active molecules for flow batteries under synthetic accessibility constraints. The tools developed constitute a simple, yet versatile strategy to enable model-based optimization with known experimental constraints, contributing to its applicability as a core component of autonomous platforms for scientific discovery.

Related papers

• Quantum-inspired Reinforcement Learning for Synthesizable Drug Design [20.00111975801053]
  We introduce a novel approach using the reinforcement learning method with quantum-inspired simulated annealing policy neural network to navigate the vast discrete space of chemical structures intelligently. Specifically, we employ a deterministic REINFORCE algorithm using policy neural networks to output transitional probability to guide state transitions and local search. Our methods are evaluated with the Practical Molecular Optimization (PMO) benchmark framework with a 10K query budget.
  arXiv  Detail & Related papers  (2024-09-13T20:43:16Z)
• Adaptive Experimentation When You Can't Experiment [55.86593195947978]
  This paper introduces the emphconfounded pure exploration transductive linear bandit (textttCPET-LB) problem. Online services can employ a properly randomized encouragement that incentivizes users toward a specific treatment.
  arXiv  Detail & Related papers  (2024-06-15T20:54:48Z)
• Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties [49.351577714596544]
  We propose a human-AI collaborative Bayesian framework to incorporate expert preferences about unmeasured abstract properties into surrogate modeling. We provide an efficient strategy that can also handle any incorrect/misleading expert bias in preferential judgments.
  arXiv  Detail & Related papers  (2024-02-27T09:23:13Z)
• Neural Operators for Accelerating Scientific Simulations and Design [85.89660065887956]
  An AI framework, known as Neural Operators, presents a principled framework for learning mappings between functions defined on continuous domains. Neural Operators can augment or even replace existing simulators in many applications, such as computational fluid dynamics, weather forecasting, and material modeling.
  arXiv  Detail & Related papers  (2023-09-27T00:12:07Z)
• Online simulator-based experimental design for cognitive model selection [74.76661199843284]
  We propose BOSMOS: an approach to experimental design that can select between computational models without tractable likelihoods. In simulated experiments, we demonstrate that the proposed BOSMOS technique can accurately select models in up to 2 orders of magnitude less time than existing LFI alternatives.
  arXiv  Detail & Related papers  (2023-03-03T21:41:01Z)
• The Struggle for Existence: Time, Memory and Bloat [0.0]
  implicit, endogenous, evolutionary optimization of genetic programs for study of biological systems is tested. Novel solutions confirm the creativity of the optimization process and provide an unique opportunity to experimentally test the neutral code. Use of this implicit, genetically, spatially programmed agents is shown to be novel, consistent with biological systems, and effective and efficient in discovering fit and novel solutions.
  arXiv  Detail & Related papers  (2023-02-06T19:53:59Z)
• Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding [48.7576911714538]
  Finding the optimal process parameters for an adhesive bonding process is challenging. Traditional evolutionary approaches (such as genetic algorithms) are then ill-suited to solve the problem. In this research, we successfully applied specific machine learning techniques to emulate the objective and constraint functions.
  arXiv  Detail & Related papers  (2021-12-16T10:14:39Z)
• Golem: An algorithm for robust experiment and process optimization [0.0]
  Design of experiment and optimization algorithms are often adopted to solve these tasks efficiently. Golem is an algorithm that is suboptimal to the choice of experiment planning strategy. It can be used to analyze the robustness of past experiments, or to guide experiment planning toward robust solutions on the fly.
  arXiv  Detail & Related papers  (2021-03-05T15:00:34Z)
• Olympus: a benchmarking framework for noisy optimization and experiment planning [0.0]
  Experiment planning strategies based on off-the-shelf optimization algorithms can be employed in fully autonomous research platforms. It is unclear how their performance would translate to noisy, higher-dimensional experimental tasks. We introduce Olympus, a software package that provides a consistent and easy-to-use framework for benchmarking optimization algorithms.
  arXiv  Detail & Related papers  (2020-10-08T17:51:51Z)
• AdaLead: A simple and robust adaptive greedy search algorithm for sequence design [55.41644538483948]
  We develop an easy-to-directed, scalable, and robust evolutionary greedy algorithm (AdaLead) AdaLead is a remarkably strong benchmark that out-competes more complex state of the art approaches in a variety of biologically motivated sequence design challenges.
  arXiv  Detail & Related papers  (2020-10-05T16:40:38Z)

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.