arXiv subjects

Benji Maruyama

Publications and source records attributed to Benji Maruyama.

8 recordsLinked to original sources

arXiv2026

ARES OS 2.0: An Orchestration Software Suite for Autonomous Experimentation Systems and Self-Driving Labs

ARES OS 2.0 (hereinafter ARES OS) is an open-source software suite to enable laboratory automation and closed-loop autonomous experimentation. Its function is to orchestrate experimental actions and data handoff between lab equipment, analysis routines, and experimental planning modules through a service-oriented architecture. ARES OS is abstracted to apply to general experimental flows common in materials science, chemistry, and biology and related disciplines. The core of ARES OS provides central control over all modules, along with the heavy lifting of UI creation, data management, and experimental design tools. ARES OS modules communicate with the core software over protobuf and gRPC, allowing them to be language-agnostic and user-creatable. This allows users to easily implement modules that control experimental hardware, process collected data , or plan experiments to meet their specific research needs. ARES OS lowers the barrier to entry for researchers to build their own self-driving labs, allowing them to focus on scientific programming for their use case and reducing the effort and time needed to bring an autonomous experimentation system online.

cs.CE↗

arXiv2023

Strong Fiber from Uniaxial Fullerene Supramolecules Aligned with Carbon Nanotubes

Carbon nanotube (CNT) wires approach copper's specific conductivity and surpass carbon fiber's strength, with further improvement anticipated with greater aspect ratios and incorporation of dopants with long-range structural order. Fullerenes assemble into multitudes of process-dependent supramolecular crystals and, while initially insulating, they become marginally conductive (up to 0.05 MSm$^{-1}$) and superconductive ($T_c=18^\circ$K with K and 28$^\circ$K with Rb) after doping. These were small (100's $\mu$m long), soft (hardness comparable to indium), and typically unaligned, which hindered development of fullerene based wires. Individual fullerenes were previously incorporated into CNT fibers, although randomly without self-assembly into supramolecules. Here, a simple variation in established CNT acid extrusion creates a fiber composed of uniaxial chains of aligned fullerene supramolecules, self-assembled between aligned few-walled CNT bundles. This will provide a testbed for novel fullerene wire transport and prospects in CNT wire advancement.

cond-mat.mtrl-sci↗

arXiv2023

Active Learning of Piecewise Gaussian Process Surrogates

Active learning of Gaussian process (GP) surrogates has been useful for optimizing experimental designs for physical/computer simulation experiments, and for steering data acquisition schemes in machine learning. In this paper, we develop a method for active learning of piecewise, Jump GP surrogates. Jump GPs are continuous within, but discontinuous across, regions of a design space, as required for applications spanning autonomous materials design, configuration of smart factory systems, and many others. Although our active learning heuristics are appropriated from strategies originally designed for ordinary GPs, we demonstrate that additionally accounting for model bias, as opposed to the usual model uncertainty, is essential in the Jump GP context. Toward that end, we develop an estimator for bias and variance of Jump GP models. Illustrations, and evidence of the advantage of our proposed methods, are provided on a suite of synthetic benchmarks, and real-simulation experiments of varying complexity.

cs.LG↗

arXiv2021

Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains

In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems. However, very few studies have evaluated the efficiency of BO as a general optimization algorithm across a broad range of experimental materials science domains. In this work, we evaluate the performance of BO algorithms with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems, namely carbon nanotube polymer blends, silver nanoparticles, lead-halide perovskites, as well as additively manufactured polymer structures and shapes. By defining acceleration and enhancement metrics for general materials optimization objectives, we find that for surrogate model selection, Gaussian Process (GP) with anisotropic kernels (automatic relevance detection, ARD) and Random Forests (RF) have comparable performance and both outperform the commonly used GP without ARD. We discuss the implicit distributional assumptions of RF and GP, and the benefits of using GP with anisotropic kernels in detail. We provide practical insights for experimentalists on surrogate model selection of BO during materials optimization campaigns.

cond-mat.mtrl-sci↗

arXiv2020

Gaussian process surrogate modeling with manipulating factors for carbon nanotube growth experiments

This paper presents a new Gaussian process (GP) surrogate modeling for predicting the outcome of a physical experiment where some experimental inputs are controlled by other manipulating factors. Particularly, we are interested in the case where the control precision is not very high, so the input factor values vary significantly even under the same setting of the corresponding manipulating factors. The case is observed in our main application to carbon nanotube growth experiments, where one experimental input among many is manipulated by another manipulating factors, and the relation between the input and the manipulating factors significantly varies in the dates and times of operations. Due to this variation, the standard GP surrogate that directly relates the manipulating factors to the experimental outcome does not provide a great predictive power on the outcome. At the same time, the GP model relating the main factors to the outcome directly is not appropriate for the prediction purpose because the main factors cannot be accurately set as planned for a future experiment. Motivated by the carbon nanotube example, we propose a two-tiered GP model, where the bottom tier relates the manipulating factors to the corresponding main factors with potential biases and variation independent of the manipulating factors, and the top tier relates the main factors to the experimental outcome. Our two-tier model explicitly models the propagation of the control uncertainty to the experimental outcome through the two GP modeling tiers. We present the inference and hyper-parameter estimation of the proposed model. The proposed approach is illustrated with the motivating example of a closed-loop autonomous research system for carbon nanotube growth experiments, and the test results are reported with the comparison to a benchmark method, i.e. a standard GP model.

stat.ME↗

arXiv2019

Spectroscopic Evaluation of Charge-transfer Doping and Strain in Graphene/MoS2 Heterostructures

It is important to study the van der Waals interface in emerging vertical heterostructures based on layered two-dimensional (2D) materials. Being atomically thin, 2D materials are susceptible to significant strains as well as charge transfer doping across the interfaces. Here we use Raman and photoluminescence (PL) spectroscopy to study the interface between monolayer graphene/MoS2 heterostructures prepared by mechanical exfoliation and layer-by-layer transfer. By using correlation analysis between the Raman modes of graphene and MoS2 we show that both layers are subjected to compressive strain and charge transfer doping following mechanical exfoliation and thermal annealing. Furthermore, we show that both strain and carrier concentration can be modulated in the heterostructures with additional thermal annealing. Our study highlights the importance of considering both mechanical and electronic coupling when characterizing the interface in van der Waals heterostructures, and demonstrates a method to tune their electromechanical properties.

cond-mat.mes-hall↗

arXiv2019

Sequential Adaptive Design for Jump Regression Estimation

Selecting input variables or design points for statistical models has been of great interest in adaptive design and active learning. Motivated by two scientific examples, this paper presents a strategy of selecting the design points for a regression model when the underlying regression function is discontinuous. The first example we undertook was for the purpose of accelerating imaging speed in a high resolution material imaging; the second was use of sequential design for the purpose of mapping a chemical phase diagram. In both examples, the underlying regression functions have discontinuities, so many of the existing design optimization approaches cannot be applied because they mostly assume a continuous regression function. Although some existing adaptive design strategies developed from treed regression models can handle the discontinuities, the Bayesian approaches come with computationally expensive Markov Chain Monte Carlo techniques for posterior inferences and subsequent design point selections, which is not appropriate for the first motivating example that requires computation at least faster than the original imaging speed. In addition, the treed models are based on the domain partitioning that are inefficient when the discontinuities occurs over complex sub-domain boundaries. We propose a simple and effective adaptive design strategy for a regression analysis with discontinuities: some statistical properties with a fixed design will be presented first, and then these properties will be used to propose a new criterion of selecting the design points for the regression analysis. Sequential design with the new criterion will be presented with comprehensive simulated examples, and its application to the two motivating examples will be presented.

stat.ML↗

arXiv2010

Double resonance Raman study of disorder in CVD-grown single-walled carbon nanotubes

Single-walled carbon nanotubes (SWNTs) with varying degrees of disorder were investigated using multiple-excitation Raman spectroscopy. The lattice disorder was imparted into the nanotubes by the addition of varying amounts of sulfur to the iron catalyst in a thermal chemical vapor deposition process. Changes in the intensities of peaks occurring due to a double resonance Raman process were studied. The intensity of the disorder-induced D band increased with a decrease in the sulfur content. Upon post-synthesis heat treatment, the double resonance process got quenched due to defect healing. The second order G' band and iTOLA bands exhibited a two-peak structure, of which one of the peaks is relatively more sensitive to defects and decreased in intensity with heat treatment.

cond-mat.mes-hall↗