26 papers · ranked by Valyu relevance
Helge S. Stein, John M. Gregoire
Integrating automation with artificial intelligence will enable scientists to spend more time identifying important problems and communicating critical insights, accelerating discovery and development of materials for emerging and future technologies.
Kyle A. O’Connell, Zelaikha B. Yosufzai, Ross A. Campbell, Collin J. Lobb + 7 more
'Collin J. Lobb' 'Haley T. Engelken' 'Laura M. Gorrell' 'Thad B. Carlson' 'Josh J. Catana' 'Dina Mikdadi' 'Vivien R. Bonazzi' 'Juergen A. Klenk'] Background As genome sequencing becomes better integrated into scientific research, government policy, and personalized medicine, the primary challenge for researchers is…
Shizhao Lu, Tanny Chavez, Wiebke Koepp, Guanhua Hao + 2 more
'Petrus H. Zwart' 'Alexander Hexemer'] MLExchange is a machine learning (ML) operations platform providing web user-interfaces (UIs) for data visualization and analysis pipelines at synchrotron facilities. Among these UIs is the segmentation app which helps synchrotron users utilize ML algorithms to automatically…
Dan Guevarra, Kevin Kan, Yungchieh Lai, Ryan Jones + 5 more
Advancements in artificial intelligence (AI) for science are continually expanding the value proposition for automation in materials and chemistry experiments. The advent of hierarchical decision-making also motivates automation of not only the individual measurements but also the coordination among multiple research…
Xiaoda Wang, Yuan Tang, Tengda Guo, Bo Sang + 5 more
'Ke Zhang' 'Jiang Qian' 'Mingjie Tang'] Abstract—Machine Learning (ML) has become ubiquitous, fueling data-driven applications across various organizations. Contrary to the traditional perception of ML in research, ML workflows can be complex, resource-intensive, and timeconsuming. Expanding an ML workflow to encompass…
Valentin Carl, Trever Schirmer, Tobias Pfandzelter, David Bermbach
Function-as-a-Service (FaaS) is a popular cloud computing model in which applications are implemented as workflows of multiple independent functions. While cloud providers usually offer composition services for such workflows, they do not support cross-platform workflows forcing developers to hard-code the composition…
Kevin Kang, Jinwen Wo, Jon Jiang, Zhong Wang
We propose Adaptive Container Service (ACS), a new paradigm for deploying bioinformatics workflows in cloud computing environments. By encapsulating the entire workflow within a single virtual container, combined with automatic workflow checkpointing and dynamic migration to appropriately scaled containers, ACS-based…
Chaudhry, Gohar Irfan, Choukse, Esha + 10 more
Agentic workflows commonly coordinate multiple models and tools with complex control logic. They are quickly becoming the dominant paradigm for AI applications. However, serving them remains inefficient with today's frameworks. The key problem is that they expose workflows as opaque sequences of model and tool calls…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Monika Vogler, Jonas Busk, Hamidreza Hajiyani, Peter Bjørn Jørgensen + 9 more
The future of materials science is borderless, cooperative, and distributed across the globe. This necessitates flexible, reconfigurable software defined research workflows, which we herein demonstrate by integrating multiple disciplines and modalities. Our brokering approach to research orchestration exposes entire…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Ozgur O. Kilic, Tianle Wang, Matteo Turilli, М. И. Титов + 3 more
Representations of Scientific Workflows Authors: ['Ozgur O. Kilic' 'Tianle Wang' 'Matteo Turilli' 'М. И. Титов' 'André Merzky' 'Line Pouchard' 'Shantenu Jha'] Abstract—Workflows are critical for scientific discovery. However, the sophistication, heterogeneity, and scale of workflows make building, testing, and…
Gaurav Kaushik, Sinisa Ivkovic, Janko Simonovic, Nebojsa Tijanic + 2 more
As biomedical data becomes increasingly easy to generate in large quantities, the methods used to analyze it have proliferated rapidly. However, for the insights gained from these analyses to be meaningful, the analysis methods themselves must be transparent and reproducible. To address this issue, numerous groups have…
Taylor Reiter, Phillip T. Brooks, Luiz Irber, Shannon E.K. Joslin + 4 more
As the scale of biological data generation has increased, the bottleneck of research has shifted from data generation to analysis. Researchers commonly need to build computational workflows that include multiple analytic tools and require incremental development as experimental insights demand tool and parameter…
Tibor Šimko, Lukas Alexander Heinrich, Clemens Lange, Adelina Eleonora Lintuluoto + 5 more
'Adelina Eleonora Lintuluoto' 'Danika Marina MacDonell' 'Audrius Mečionis' 'Diego Rodríguez Rodríguez' 'Parth Shandilya' 'Marco Vidal García'] We describe a novel approach for experimental High-Energy Physics (HEP) data analyses that is centred around the declarative rather than imperative paradigm when describing…
Tazro Ohta, Tomoya Tanjo, Osamu Ogasawara
CWL-metrics enables users to choose an appropriate cloud instance on which to run workflows, based on runtime metrics data. Metrics data summarized by workflow inputs, such as the number of threads to use or total file size of input data, inform more efficient cloud use for research projects. Each user can perform…
Peter Kraus, Edan Bainglass, Francisco F. Ramirez, Enea Svaluto-Ferro + 7 more
Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by a full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We…
Min Cui, Yipeng Wang
Workflow scheduling in cloud computing is attracting increasing attention. Cloud computing can assign tasks to available virtual machine resources in cloud data centers according to scheduling strategies, providing a powerful computing platform for the execution of workflow tasks. However, developing effective workflow…
Rama El-khawaldeh, Mason Guy, Finn Bork, Nina Taherimakhsousi + 6 more
This work presents a generalizable computer vision (CV) and machine learning model that is used for automated real-time monitoring and control of a diverse array of workup processes. Our system simultaneously monitors multiple physical parameters (e.g., liquid level, homogeneity, turbidity, solid, residue, and color)…
Georgia Kougka, Anastasios Gounaris, Alkis Simitsis
Workflow technology is rapidly evolving and, rather than being limited to modeling the control flow in business processes, is becoming a key mechanism to perform advanced data management, such as big data analytics. This survey focuses on data-centric workflows (or workflows for data analytics or data flows), where a…
Samuel Lampa, Martin Dahlö, Jonathan Alvarsson, Ola Spjuth
The complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines and assist with job automation and aids reproducibility of analyses. Many contemporary workflow tools are specialized and not designed…
Patrick McKeever, Varun Mittal, Bryce Fukuda, Ka Yee Yeung + 1 more
The exponential growth of omics data requires novel strategies for storage, transfer, and processing of said data. We present a scheduler based on the Temporal.io workflow framework which enables two key optimizations of bioinformatics workflows. Firstly, we enable users to transparently map workflow steps to diverse…
Ward Jaradat, Alan Dearle, Adam Barker
—Orchestrating service-oriented workflows is typically based on a design model that routes both data and control through a single point – the centralised workflow engine. This causes scalability problems that include the unnecessary consumption of the network bandwidth, high latency in transmitting data between the…
Jan Janssen, Janine George, Julian Geiger, Marnik Bercx + 7 more
Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow…
Mahnoor Zulfiqar, Michael R. Crusoe, Birgitta König-Ries, Christoph Steinbeck + 2 more
Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…