8 research outputs found
Decreasing the Computing Time of Bayesian Optimization using Generalizable Memory Pruning
Bayesian optimization (BO) suffers from long computing times when processing
highly-dimensional or large data sets. These long computing times are a result
of the Gaussian process surrogate model having a polynomial time complexity
with the number of experiments. Running BO on high-dimensional or massive data
sets becomes intractable due to this time complexity scaling, in turn,
hindering experimentation. Alternative surrogate models have been developed to
reduce the computing utilization of the BO procedure, however, these methods
require mathematical alteration of the inherit surrogate function, pigeonholing
use into only that function. In this paper, we demonstrate a generalizable BO
wrapper of memory pruning and bounded optimization, capable of being used with
any surrogate model and acquisition function. Using this memory pruning
approach, we show a decrease in wall-clock computing times per experiment of BO
from a polynomially increasing pattern to a sawtooth pattern that has a
non-increasing trend without sacrificing convergence performance. Furthermore,
we illustrate the generalizability of the approach across two unique data sets,
two unique surrogate models, and four unique acquisition functions. All model
implementations are run on the MIT Supercloud state-of-the-art computing
hardware.Comment: Accepted as a paper in IEEE HPEC 202
Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)
Needle-in-a-Haystack problems exist across a wide range of applications
including rare disease prediction, ecological resource management, fraud
detection, and material property optimization. A Needle-in-a-Haystack problem
arises when there is an extreme imbalance of optimum conditions relative to the
size of the dataset. For example, only out of k total materials
in the open-access Materials Project database have a negative Poisson's ratio.
However, current state-of-the-art optimization algorithms are not designed with
the capabilities to find solutions to these challenging multidimensional
Needle-in-a-Haystack problems, resulting in slow convergence to a global
optimum or pigeonholing into a local minimum. In this paper, we present a
Zooming Memory-Based Initialization algorithm, entitled ZoMBI. ZoMBI actively
extracts knowledge from the previously best-performing evaluated experiments to
iteratively zoom in the sampling search bounds towards the global optimum
"needle" and then prunes the memory of low-performing historical experiments to
accelerate compute times by reducing the algorithm time complexity from
to for forward experiments per activation, which
trends to a constant over several activations. Additionally, ZoMBI
implements two custom adaptive acquisition functions to further guide the
sampling of new experiments toward the global optimum. We validate the
algorithm's optimization performance on three real-world datasets exhibiting
Needle-in-a-Haystack and further stress-test the algorithm's performance on an
additional 174 analytical datasets. The ZoMBI algorithm demonstrates compute
time speed-ups of 400x compared to traditional Bayesian optimization as well as
efficiently discovering optima in under 100 experiments that are up to 3x more
highly optimized than those discovered by similar methods MiP-EGO, TuRBO, and
HEBO.Comment: Paper 16 pages; SI 6 page
Vision-driven Autocharacterization of Perovskite Semiconductors
In materials research, the task of characterizing hundreds of different
materials traditionally requires equally many human hours spent measuring
samples one by one. We demonstrate that with the integration of computer vision
into this material research workflow, many of these tasks can be automated,
significantly accelerating the throughput of the workflow for scientists. We
present a framework that uses vision to address specific pain points in the
characterization of perovskite semiconductors, a group of materials with the
potential to form new types of solar cells. With this approach, we automate the
measurement and computation of chemical and optoelectronic properties of
perovskites. Our framework proposes the following four key contributions: (i) a
computer vision tool for scalable segmentation to arbitrarily many material
samples, (ii) a tool to extract the chemical composition of all material
samples, (iii) an algorithm capable of automatically computing band gap across
arbitrarily many unique samples using vision-segmented hyperspectral
reflectance data, and (iv) automating the stability measurement of multi-hour
perovskite degradation experiments with vision for spatially non-uniform
samples. We demonstrate the key contributions of the proposed framework on
eighty samples of unique composition from the formamidinium-methylammonium lead
tri-iodide perovskite system and validate the accuracy of each method using
human evaluation and X-ray diffraction.Comment: Manuscript 8 pages; Supplemental 7 page
Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark
We provide a new multi-task benchmark for evaluating text-to-image models. We
perform a human evaluation comparing the most common open-source (Stable
Diffusion) and commercial (DALL-E 2) models. Twenty computer science AI
graduate students evaluated the two models, on three tasks, at three difficulty
levels, across ten prompts each, providing 3,600 ratings. Text-to-image
generation has seen rapid progress to the point that many recent models have
demonstrated their ability to create realistic high-resolution images for
various prompts. However, current text-to-image methods and the broader body of
research in vision-language understanding still struggle with intricate text
prompts that contain many objects with multiple attributes and relationships.
We introduce a new text-to-image benchmark that contains a suite of thirty-two
tasks over multiple applications that capture a model's ability to handle
different features of a text prompt. For example, asking a model to generate a
varying number of the same object to measure its ability to count or providing
a text prompt with several objects that each have a different attribute to
identify its ability to match objects and attributes correctly. Rather than
subjectively evaluating text-to-image results on a set of prompts, our new
multi-task benchmark consists of challenge tasks at three difficulty levels
(easy, medium, and hard) and human ratings for each generated image.Comment: NeurIPS 2022 Workshop on Human Evaluation of Generative Models (HEGM
A System for High-Throughput Materials Exploration Driven by Machine Learning
Functional materials have vast and high-dimensional compositions spaces which make discovering optimized compositions intractable with conventional synthesis tools. Conventional experimental methods of exploring material composition spaces are slow and resource intensive due to being manual processes and requiring trial-and-error experimentation. Thus, the question is posed: How can we design an optimized functional material from this highly dimensional and vast composition space such that it has a high performance for a given application?
In this thesis, machine learning algorithms are integrated into novel, high-throughput synthesis hardware to accelerate the rate of material composition exploration by 10000x relative to these conventional methods. First, a novel inkjet droplet deposition hardware system is constructed to generate arrays of unique functional material compositions in the form of droplets using fluid mechanics and motor control theory. Second, computer vision and Bayesian optimization machine learning algorithms are integrated into the droplet synthesis loop to autonomously discover synthesis conditions that generate optimized droplets without any intervention of a domain expert. Third, mulitphysics models are developed to simulate the performance of functional material devices within the gamut of environmental conditions without having to run expensive laboratory experiments. The culmination of these three processes developed in this master's thesis provide validated methods for driving high-throughput, low-cost materials exploration and optimization to be further explored in my doctoral thesis.S.M
Autonomous Optimization of Fluid Systems at Varying Length Scales
Autonomous optimization is a process by which hardware conditions are
discovered that generate an optimized experimental product without the guidance
of a domain expert. We design an autonomous optimization framework to discover
the experimental conditions within fluid systems that generate discrete and
uniform droplet patterns. Generating discrete and uniform droplets requires
high-precision control over the experimental conditions of a fluid system.
Fluid stream instabilities, such as Rayleigh-Plateau instability and capillary
instability, drive the separation of a flow into individual droplets. However,
because this phenomenon leverages an instability, by nature the hardware must
be precisely tuned to achieve uniform, repeatable droplets. Typically this
requires a domain expert in the loop and constant re-tuning depending on the
hardware configuration and liquid precursor selection. Herein, we propose a
computer vision-driven Bayesian optimization framework to discover the precise
hardware conditions that generate uniform, reproducible droplets with the
desired features, leveraging flow instability without a domain expert in the
loop. This framework is validated on two fluid systems, at the micrometer and
millimeter length scales, using microfluidic and inkjet systems, respectively,
indicating the application breadth of this approach.Comment: 8 page
Vision-driven Autocharacterization of Perovskite Semiconductors
In materials research, the task of characterizing hundreds of different materials traditionally requires equally many human hours spent measuring samples one by one. We demonstrate that with the integration of computer vision into this material research workflow, many of these tasks can be automated, significantly accelerating the throughput of the workflow for scientists. We present a framework that uses vision to address specific pain points in the characterization of perovskite semiconductors, a group of materials with the potential to form new types of solar cells. With this approach, we automate the measurement and computation of chemical and optoelectronic properties of perovskites. Our framework proposes the following four key contributions: (i) a computer vision tool for scalable segmentation to arbitrarily many material samples, (ii) a tool to extract the chemical composition of all material samples, (iii) an algorithm capable of automatically computing band gap across arbitrarily many unique samples using vision-segmented hyperspectral reflectance data, and (iv) automating the stability measurement of multi-hour perovskite degradation experiments with vision for spatially non-uniform samples. We demonstrate the key contributions of the proposed framework on eighty samples of unique composition from the formamidinium-methylammonium lead tri-iodide perovskite system and validate the accuracy of each method using human evaluation and X-ray diffraction
An Open-Source Environmental Chamber for Materials-Stability Testing Using an Optical Proxy
This study is motivated by the desire to disseminate a low-cost, high-precision, high-throughput environmental chamber to test materials and devices under elevated humidity, temperature, and light. This paper documents the creation of an open-source tool with a bill of materials as low as US$2,000, and the subsequent evolution of three second-generation tools installed at three different universities spanning thin films, bulk crystals, and thin-film solar-cell devices. We introduce an optical proxy measurement to detect real-time phase changes in materials. We present correlations between this optical proxy and standard X-ray diffraction measurements, describe some edge cases where the proxy measurement fails, and report key learnings from the technology-translation process. By sharing lessons learned, we hope that future open-hardware development and translation efforts can proceed with reduced friction. Throughout the paper, we provide examples of scientific impact, wherein participating laboratories used their environmental chambers to study and improve the stabilities of halide-perovskite materials. All generations of hardware bills of materials, assembly instructions, and operating codes are available in open-source repositories