Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping

• URL: http://arxiv.org/abs/2306.10406v1
• Date: Sat, 17 Jun 2023 18:25:32 GMT
• Title: Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping
• Authors: Felix Adams, Austin McDannald, Ichiro Takeuchi, A. Gilad Kusne
• Abstract summary: We present a set of methods for integrating human input into an autonomous materials exploration campaign. The user can choose to provide input by indicating regions of interest, likely regions, and likely phase boundaries. We demonstrate a significant improvement in phase mapping performance given appropriate human input.
• Score: 14.930208990741129
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous experimentation (AE) combines machine learning and research hardware automation in a closed loop, guiding subsequent experiments toward user goals. As applied to materials research, AE can accelerate materials exploration, reducing time and cost compared to traditional Edisonian studies. Additionally, integrating knowledge from diverse sources including theory, simulations, literature, and domain experts can boost AE performance. Domain experts may provide unique knowledge addressing tasks that are difficult to automate. Here, we present a set of methods for integrating human input into an autonomous materials exploration campaign for composition-structure phase mapping. The methods are demonstrated on x-ray diffraction data collected from a thin film ternary combinatorial library. At any point during the campaign, the user can choose to provide input by indicating regions-of-interest, likely phase regions, and likely phase boundaries based on their prior knowledge (e.g., knowledge of the phase map of a similar material system), along with quantifying their certainty. The human input is integrated by defining a set of probabilistic priors over the phase map. Algorithm output is a probabilistic distribution over potential phase maps, given the data, model, and human input. We demonstrate a significant improvement in phase mapping performance given appropriate human input.

Related papers

• ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis [5.012314384895538]
  We argue that not all of the essential features of what makes an operation important can be accurately captured mathematically using rewards. We propose an AutoEDA model trained through imitation learning from expert EDA sessions, bypassing the need for manually defined interestingness measures. Our method outperforms the existing state-of-the-art end-to-end EDA approach on benchmarks by upto 3x, showing strong performance and generalization.
  arXiv  Detail & Related papers  (2024-10-15T04:56:13Z)
• Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunities [0.2445561610325265]
  Here, we explore interactive building on multi-fidelity BO (MFBO), starting with classical (data-driven) MFBO, then structured (physics-driven) sMFBO, and extending it to allow human in the loop interactive iMFBO for adaptive and domain expert aligned exploration. Detailed analysis and comparison show the impact of physics knowledge injection and on-the-fly human decisions for improved exploration, current challenges, and potential opportunities for algorithm development with combining data, physics and real time human decisions.
  arXiv  Detail & Related papers  (2024-02-20T22:12:33Z)
• Gaussian Mixture Models for Affordance Learning using Bayesian Networks [50.18477618198277]
  Affordances are fundamental descriptors of relationships between actions, objects and effects. This paper approaches the problem of an embodied agent exploring the world and learning these affordances autonomously from its sensory experiences.
  arXiv  Detail & Related papers  (2024-02-08T22:05:45Z)
• Physics Inspired Hybrid Attention for SAR Target Recognition [61.01086031364307]
  We propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the issues. PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target. Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters.
  arXiv  Detail & Related papers  (2023-09-27T14:39:41Z)
• Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning [60.599223456298915]
  We propose a novel way to integrate deep learning into exploration by leveraging 3D scene completion for informed, safe, and interpretable mapping and planning. We show that our method can speed up coverage of an environment by 73% compared to the baselines with only minimal reduction in map accuracy. Even if scene completions are not included in the final map, we show that they can be used to guide the robot to choose more informative paths, speeding up the measurement of the scene with the robot's sensors by 35%.
  arXiv  Detail & Related papers  (2022-08-17T14:19:33Z)
• Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping [10.629434761354938]
  Science-informed AI or scientific AI has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous systems. The CAMEO algorithm employs scientific AI to address two tasks: learning a material system's composition-structure relationship and identifying materials compositions with optimal functional properties.
  arXiv  Detail & Related papers  (2021-11-15T00:48:34Z)
• Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else? [93.91375268580806]
  Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms. Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications. By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
  arXiv  Detail & Related papers  (2021-11-09T13:30:34Z)
• Combining Feature and Instance Attribution to Detect Artifacts [62.63504976810927]
  We propose methods to facilitate identification of training data artifacts. We show that this proposed training-feature attribution approach can be used to uncover artifacts in training data. We execute a small user study to evaluate whether these methods are useful to NLP researchers in practice.
  arXiv  Detail & Related papers  (2021-07-01T09:26:13Z)
• Statistical Approach to Quantum Phase Estimation [62.92678804023415]
  We introduce a new statistical and variational approach to the phase estimation algorithm (PEA) Unlike the traditional and iterative PEAs which return only an eigenphase estimate, the proposed method can determine any unknown eigenstate-eigenphase pair. We show the simulation results of the method with the Qiskit package on the IBM Q platform and on a local computer.
  arXiv  Detail & Related papers  (2021-04-21T00:02:00Z)
• Distributed Learning via Filtered Hyperinterpolation on Manifolds [2.2046162792653017]
  This paper studies the problem of learning real-valued functions on manifold. Motivated by the problem of handling large data sets, it presents a parallel data processing approach. We prove quantitative relations between the approximation quality of the learned function over the entire manifold.
  arXiv  Detail & Related papers  (2020-07-18T10:05:18Z)
• Unsupervised phase discovery with deep anomaly detection [0.0]
  We demonstrate how to explore phase diagrams with automated and unsupervised machine learning. We employ deep neural networks to determine the entire phase diagram in a completely unsupervised and automated fashion. Our method allows us to reveal a phase-separated region between supersolid and superfluid parts with unexpected properties.
  arXiv  Detail & Related papers  (2020-03-22T14:20:04Z)

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.