Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels

• URL: http://arxiv.org/abs/2006.02489v1
• Date: Wed, 3 Jun 2020 19:18:47 GMT
• Title: Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels
• Authors: Marcus M. Noack, Gregory S. Doerk, Ruipeng Li, Jason K. Streit, Richard A. Vaia, Kevin G. Yager, Masafumi Fukuto
• Abstract summary: A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Recent advances have led to an increase in efficiency of materials discovery by increasingly automating the exploration processes. Gamma process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments.
• Score: 1.976226676686868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gives rise to particularly vast parameter spaces. Recent advances have led to an increase in efficiency of materials discovery by increasingly automating the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored efficiently and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. Gaussian process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments. We have recently demonstrated the positive impact of GPR-driven decision-making algorithms on autonomously steering experiments at a synchrotron beamline. However, due to the complexity of the experiments, GPR often cannot be used in its most basic form, but rather has to be tuned to account for the special requirements of the experiments. Two requirements seem to be of particular importance, namely inhomogeneous measurement noise (input dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tackle in this paper. Our synthetic and experimental tests demonstrate the importance of both concepts for experiments in materials science and the benefits that result from including them in the autonomous decision-making process.

Related papers

• Targeted Sequential Indirect Experiment Design [4.262342157729123]
  hypotheses concern specific aspects of complex, imperfectly understood or entirely unknown mechanisms. Experiments can not be conducted directly on the target variables of interest, but are indirect. We develop an adaptive strategy to design indirect experiments that optimally inform a targeted query about the ground truth mechanism.
  arXiv  Detail & Related papers  (2024-05-30T12:14:25Z)
• LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
  We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework. We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
  arXiv  Detail & Related papers  (2024-05-16T03:04:10Z)
• MLXP: A Framework for Conducting Replicable Experiments in Python [63.37350735954699]
  We propose MLXP, an open-source, simple, and lightweight experiment management tool based on Python. It streamlines the experimental process with minimal overhead while ensuring a high level of practitioner overhead.
  arXiv  Detail & Related papers  (2024-02-21T14:22:20Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics [54.172707311728885]
  We introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED) Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time.
  arXiv  Detail & Related papers  (2023-06-03T06:19:20Z)
• Pitfalls in Experiments with DNN4SE: An Analysis of the State of the Practice [0.7614628596146599]
  We conduct a mapping study, examining 194 experiments with techniques that rely on deep neural networks appearing in 55 papers published in premier software engineering venues. Our study reveals that most of the experiments, including those that have received ACM artifact badges, have fundamental limitations that raise doubts about the reliability of their findings.
  arXiv  Detail & Related papers  (2023-05-19T09:55:48Z)
• Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes [4.18804572788063]
  This thesis aims to use GP modelling to reason about the latent emission signature from the Seyfert galaxy Markarian 335. The second contribution is to extend the GP framework to molecular and chemical reaction representations and to provide an open-source software library to enable the framework to be used by scientists. The fourth contribution is to introduce a Bayesian optimisation scheme capable of modelling aleatoric uncertainty to facilitate the identification of material compositions that possess intrinsic robustness to large scale fabrication processes.
  arXiv  Detail & Related papers  (2023-03-24T22:20:14Z)
• GFlowNets for AI-Driven Scientific Discovery [74.27219800878304]
  We present a new probabilistic machine learning framework called GFlowNets. GFlowNets can be applied in the modeling, hypotheses generation and experimental design stages of the experimental science loop. We argue that GFlowNets can become a valuable tool for AI-driven scientific discovery.
  arXiv  Detail & Related papers  (2023-02-01T17:29:43Z)
• Active learning-assisted neutron spectroscopy with log-Gaussian processes [0.0]
  A number of scientific problems require searching for signals, which may be time consuming and inefficient if done manually. Here, we describe a probabilistic active learning approach that not only runs autonomously, but can also directly provide locations for informative measurements. The resulting benefits can be demonstrated on a real TAS experiment and a benchmark including numerous different excitations.
  arXiv  Detail & Related papers  (2022-09-02T12:20:52Z)
• Active Exploration via Experiment Design in Markov Chains [86.41407938210193]
  A key challenge in science and engineering is to design experiments to learn about some unknown quantity of interest. We propose an algorithm that efficiently selects policies whose measurement allocation converges to the optimal one. In addition to our theoretical analysis, we showcase our framework on applications in ecological surveillance and pharmacology.
  arXiv  Detail & Related papers  (2022-06-29T00:04:40Z)

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.