Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries
- URL: http://arxiv.org/abs/2412.18067v1
- Date: Tue, 24 Dec 2024 00:39:51 GMT
- Title: Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries
- Authors: Yu Liu, Rohit Pant, Ichiro Takeuchi, R. Jackson Spurling, Jon-Paul Maria, Maxim Ziatdinov, Sergei V. Kalinin,
- Abstract summary: Combinatorial libraries are a powerful approach for exploring the evolution of physical properties across binary and ternary cross-sections.
Scanning Probe Microscopies (SPM) offer significant potential for quantitative, functionally relevant combi-library readouts.
- Score: 14.028387934700222
- License:
- Abstract: Combinatorial libraries are a powerful approach for exploring the evolution of physical properties across binary and ternary cross-sections in multicomponent phase diagrams. Although the synthesis of these libraries has been developed since the 1960s and expedited with advanced laboratory automation, the broader application of combinatorial libraries relies on fast, reliable measurements of concentration-dependent structures and functionalities. Scanning Probe Microscopies (SPM), including piezoresponse force microscopy (PFM), offer significant potential for quantitative, functionally relevant combi-library readouts. Here we demonstrate the implementation of fully automated SPM to explore the evolution of ferroelectric properties in combinatorial libraries, focusing on Sm-doped BiFeO3 and ZnxMg1-xO systems. We also present and compare Gaussian Process-based Bayesian Optimization models for fully automated exploration, emphasizing local reproducibility (effective noise) as an essential factor in optimal experiment workflows. Automated SPM, when coupled with upstream synthesis controls, plays a pivotal role in bridging materials synthesis and characterization.
Related papers
- Multi-Agent Sampling: Scaling Inference Compute for Data Synthesis with Tree Search-Based Agentic Collaboration [81.45763823762682]
This work aims to bridge the gap by investigating the problem of data synthesis through multi-agent sampling.
We introduce Tree Search-based Orchestrated Agents(TOA), where the workflow evolves iteratively during the sequential sampling process.
Our experiments on alignment, machine translation, and mathematical reasoning demonstrate that multi-agent sampling significantly outperforms single-agent sampling as inference compute scales.
arXiv Detail & Related papers (2024-12-22T15:16:44Z) - Unlocking Potential Binders: Multimodal Pretraining DEL-Fusion for Denoising DNA-Encoded Libraries [51.72836644350993]
Multimodal Pretraining DEL-Fusion model (MPDF)
We develop pretraining tasks applying contrastive objectives between different compound representations and their text descriptions.
We propose a novel DEL-fusion framework that amalgamates compound information at the atomic, submolecular, and molecular levels.
arXiv Detail & Related papers (2024-09-07T17:32:21Z) - From Text to Test: AI-Generated Control Software for Materials Science Instruments [0.0]
Large language models (LLMs) are transforming the landscape of chemistry and materials science.
Here, we demonstrate the rapid deployment of a Python-based control module for a Keithley 2400 electrical source measure unit.
arXiv Detail & Related papers (2024-06-23T21:32:57Z) - Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries [11.98551464809572]
We propose and implement a co-orchestration approach for conducting measurements with complex observables such as spectra or images.
The method relies on combining dimensionality reduction by variational autoencoders with representation learning for control over the latent space structure.
The proposed framework can be extended to multiple measurement modalities and arbitrary dimensionality of measured signals.
arXiv Detail & Related papers (2024-02-03T16:03:17Z) - Closing the loop: Autonomous experiments enabled by
machine-learning-based online data analysis in synchrotron beamline
environments [80.49514665620008]
Machine learning can be used to enhance research involving large or rapidly generated datasets.
In this study, we describe the incorporation of ML into a closed-loop workflow for X-ray reflectometry (XRR)
We present solutions that provide an elementary data analysis in real time during the experiment without introducing the additional software dependencies in the beamline control software environment.
arXiv Detail & Related papers (2023-06-20T21:21:19Z) - A dynamic Bayesian optimized active recommender system for
curiosity-driven Human-in-the-loop automated experiments [8.780395483188242]
We present the development of a new type of human in the loop experimental workflow, via a Bayesian optimized active recommender system (BOARS)
This work shows the utility of human-augmented machine learning approaches for curiosity-driven exploration of systems across experimental domains.
arXiv Detail & Related papers (2023-04-05T14:54:34Z) - An efficient graph generative model for navigating ultra-large
combinatorial synthesis libraries [1.5495593104596397]
Virtual, make-on-demand chemical libraries have transformed early-stage drug discovery by unlocking vast, synthetically accessible regions of chemical space.
Recent years have witnessed rapid growth in these libraries from millions to trillions of compounds, hiding undiscovered, potent hits for a variety of therapeutic targets.
We propose the Combinatorial Synthesis Library Variational Auto-Encoder (CSLVAE) to overcome these challenges.
arXiv Detail & Related papers (2022-10-19T15:43:13Z) - Shared Space Transfer Learning for analyzing multi-site fMRI data [83.41324371491774]
Multi-voxel pattern analysis (MVPA) learns predictive models from task-based functional magnetic resonance imaging (fMRI) data.
MVPA works best with a well-designed feature set and an adequate sample size.
Most fMRI datasets are noisy, high-dimensional, expensive to collect, and with small sample sizes.
This paper proposes the Shared Space Transfer Learning (SSTL) as a novel transfer learning approach.
arXiv Detail & Related papers (2020-10-24T08:50:26Z) - Towards an Automatic Analysis of CHO-K1 Suspension Growth in
Microfluidic Single-cell Cultivation [63.94623495501023]
We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data.
Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
arXiv Detail & Related papers (2020-10-20T08:36:51Z) - Annotating and Extracting Synthesis Process of All-Solid-State Batteries
from Scientific Literature [10.443499579567069]
We present a novel corpus of the synthesis process for all-solid-state batteries and an automated machine reading system.
We define the representation of the synthesis processes using flow graphs, and create a corpus from the experimental sections of 243 papers.
The automated machine-reading system is developed by a deep learning-based sequence tagger and simple rule-based relation extractor.
arXiv Detail & Related papers (2020-02-18T02:30:03Z) - Multilinear Compressive Learning with Prior Knowledge [106.12874293597754]
Multilinear Compressive Learning (MCL) framework combines Multilinear Compressive Sensing and Machine Learning into an end-to-end system.
Key idea behind MCL is the assumption of the existence of a tensor subspace which can capture the essential features from the signal for the downstream learning task.
In this paper, we propose a novel solution to address both of the aforementioned requirements, i.e., How to find those tensor subspaces in which the signals of interest are highly separable?
arXiv Detail & Related papers (2020-02-17T19:06:05Z)
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.