Progress of Data-driven Science in Materials Research Community

High voltage TEMs were introduced commercially thirty years ago, with the installations of 500 kV Hitachi instruments at the Universities of Nogoya and Tokyo. Since that time a total of 51 commercial instruments, having maximum accelerating potentials of 0.5-3.5 MV, have been delivered. Prices have gone from about a dollar per volt for the early instruments to roughly twenty dollars per volt today, which is not so unreasonable considerinp inflation and vastly improved electronics and other improvements. The most expensive HVEM (the 3.5 MV instrument at Osaka University) cost about 5 percent of the construction cost of the USA's latest synchrotron.Table 1 briefly traces the development of HVEM in this country for the materials sciences. There are now only three available instruments at two sites: the 1.2 MeV HVEM at Argonne National Lab, and 1.0 and 1.5 MeV instruments at Lawrence Berkeley National Lab. Fortunately, both sites are user facilities funded by DOE for the materials research community.

Download Full-text

Advocating on behalf of the materials research community

MRS Bulletin ◽

10.1557/mrs.2018.158 ◽

2018 ◽

Vol 43 (7) ◽

pp. 469-470 ◽

Cited By ~ 1

Author(s):

Sean J. Hearne

Keyword(s):

Research Community ◽

Materials Research

Download Full-text

Learning Damage Representations with Sequence-to-Sequence Models

Sensors ◽

10.3390/s22020452 ◽

2022 ◽

Vol 22 (2) ◽

pp. 452

Author(s):

Qun Yang ◽

Dejian Shen

Keyword(s):

Damage Detection ◽

Health Monitoring ◽

Research Community ◽

Regions Of Interest ◽

Data Driven ◽

Economic Losses ◽

Shake Table ◽

Learning Models ◽

Shake Table Tests ◽

Damage States

Natural hazards have caused damages to structures and economic losses worldwide. Post-hazard responses require accurate and fast damage detection and assessment. In many studies, the development of data-driven damage detection within the research community of structural health monitoring has emerged due to the advances in deep learning models. Most data-driven models for damage detection focus on classifying different damage states and hence damage states cannot be effectively quantified. To address such a deficiency in data-driven damage detection, we propose a sequence-to-sequence (Seq2Seq) model to quantify a probability of damage. The model was trained to learn damage representations with only undamaged signals and then quantify the probability of damage by feeding damaged signals into models. We tested the validity of our proposed Seq2Seq model with a signal dataset which was collected from a two-story timber building subjected to shake table tests. Our results show that our Seq2Seq model has a strong capability of distinguishing damage representations and quantifying the probability of damage in terms of highlighting the regions of interest.

Download Full-text

Collaboration between Government and Research Community to Respond to COVID-19: Israel’s Case

Journal of Open Innovation Technology Market and Complexity ◽

10.3390/joitmc7040208 ◽

2021 ◽

Vol 7 (4) ◽

pp. 208

Author(s):

Mor Peleg ◽

Amnon Reichman ◽

Sivan Shachar ◽

Tamir Gadot ◽

Meytal Avgil Tsadok ◽

...

Keyword(s):

Health Policy ◽

Data Science ◽

Research Community ◽

Remote Access ◽

Data Driven ◽

Multidisciplinary Teams ◽

Science Methods ◽

Research Environments ◽

Future Data ◽

The Government

Triggered by the COVID-19 crisis, Israel’s Ministry of Health (MoH) held a virtual datathon based on deidentified governmental data. Organized by a multidisciplinary committee, Israel’s research community was invited to offer insights to help solve COVID-19 policy challenges. The Datathon was designed to develop operationalizable data-driven models to address COVID-19 health policy challenges. Specific relevant challenges were defined and diverse, reliable, up-to-date, deidentified governmental datasets were extracted and tested. Secure remote-access research environments were established. Registration was open to all citizens. Around a third of the applicants were accepted, and they were teamed to balance areas of expertise and represent all sectors of the community. Anonymous surveys for participants and mentors were distributed to assess usefulness and points for improvement and retention for future datathons. The Datathon included 18 multidisciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5 presentation mentors, and 12 judges. The insights developed by the three winning teams are currently considered by the MoH as potential data science methods relevant for national policies. Based on participants’ feedback, the process for future data-driven regulatory responses for health crises was improved. Participants expressed increased trust in the MoH and readiness to work with the government on these or future projects.

Download Full-text

Data-Driven Bi-Objective Genetic Algorithms EvoNN and BioGP and Their Applications in Metallurgical and Materials Domain

Advances in Chemical and Materials Engineering - Computational Approaches to Materials Design ◽

10.4018/978-1-5225-0290-6.ch012 ◽

2016 ◽

pp. 346-368 ◽

Cited By ~ 6

Author(s):

Nirupam Chakraborti

Keyword(s):

Genetic Algorithm ◽

Noisy Data ◽

Prey Type ◽

Data Driven ◽

Neural Net ◽

Predator Prey ◽

Modeling And Optimization ◽

Materials Research ◽

Computational Materials ◽

Data Driven Modeling

Data-driven modeling and optimization are now of utmost importance in computational materials research. This chapter presents the operational details of two recent algorithms EvoNN (Evolutionary Neural net) and BioGP (Bi-objective Genetic Programming) which are particularly suitable for modeling and optimization tasks pertinent to noisy data. In both the approaches a tradeoff between the accuracy and complexity of the candidate models are sought, ultimately leading to some optimum tradeoffs. These novel strategies are tailor-made for constructing models of right complexity, excluding the non-essential inputs. They are constructed to implement the notion of Pareto-optimality using a predator-prey type genetic algorithm, providing the user with a set of optimum models, out of which an appropriate one can be easily picked up by applying some external criteria, if necessary. Several materials related problems have been solved using these algorithms in recent times and a couple of typical examples are briefly presented in this chapter.

Download Full-text

The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research

Computational Materials Science ◽

10.1016/j.commatsci.2020.109544 ◽

2020 ◽

Vol 176 ◽

pp. 109544 ◽

Cited By ~ 4

Author(s):

Ryan Jacobs ◽

Tam Mayeshiba ◽

Ben Afflerbach ◽

Luke Miles ◽

Max Williams ◽

...

Keyword(s):

Machine Learning ◽

Open Source ◽

Data Driven ◽

Materials Simulation ◽

Materials Research

Download Full-text

Coronavirus canSAR – a Data-Driven, AI-Enabled, Drug Discovery Resource for the Research Community

10.26434/chemrxiv.12286877 ◽

2020 ◽

Author(s):

Costas Mitsopoulos ◽

Albert A. Antolin ◽

Eloy Villasclaras Fernandez ◽

Patrizio Di Micco ◽

Ioan L. Micca ◽

...

Keyword(s):

Drug Discovery ◽

Research Community ◽

Data Driven ◽

International Effort ◽

Objective Information ◽

Great Knowledge

<p>We describe an AI-enabled, integrated Coronavirus drug discovery knowledgebase, free for the research community. Its goal is to make accessible up to date information relevant to drug discovery for SARS-CoV-2 and other coronaviruses. It builds on great knowledge from across therapeutic areas and provides unbiased, systematic, objective information to empower the international effort.</p>

Download Full-text

When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges

Applied Sciences ◽

10.3390/app11209680 ◽

2021 ◽

Vol 11 (20) ◽

pp. 9680

Author(s):

Xuan Zhou ◽

Ruimin Ke ◽

Hao Yang ◽

Chenxi Liu

Keyword(s):

Intelligent Transportation Systems ◽

Intelligent Transportation ◽

Transportation Systems ◽

Research Community ◽

Edge Computing ◽

Data Driven ◽

Future Directions ◽

Critical Part ◽

Modern Cities ◽

Computing Vision

The widespread use of mobile devices and sensors has motivated data-driven applications that can leverage the power of big data to benefit many aspects of our daily life, such as health, transportation, economy, and environment. Under the context of smart city, intelligent transportation systems (ITS), such as a main building block of modern cities and edge computing (EC), as an emerging computing service that targets addressing the limitations of cloud computing, have attracted increasing attention in the research community in recent years. It is well believed that the application of EC in ITS will have considerable benefits to transportation systems regarding efficiency, safety, and sustainability. Despite the growing trend in ITS and EC research, a big gap in the existing literature is identified: the intersection between these two promising directions has been far from well explored. In this paper, we focus on a critical part of ITS, i.e., sensing, and conducting a review on the recent advances in ITS sensing and EC applications in this field. The key challenges in ITS sensing and future directions with the integration of edge computing are discussed.

Download Full-text