scholarly journals Ashley Spear Advances Data-Driven Approaches to Materials Research

JOM ◽  
2018 ◽  
Vol 70 (8) ◽  
pp. 1353-1353
Keyword(s):  
Data Driven ◽  
Materials Research

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

2016 ◽  
pp. 346-368 ◽  
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.

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

2020 ◽  
Vol 176 ◽  
pp. 109544 ◽  
Author(s):  
Ryan Jacobs ◽  
Tam Mayeshiba ◽  
Ben Afflerbach ◽  
Luke Miles ◽  
Max Williams ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Driven ◽  
Materials Simulation ◽  
Materials Research

scholarly journals Data-driven materials research enabled by natural language processing and information extraction

2020 ◽  
Vol 7 (4) ◽  
pp. 041317
Author(s):  
Elsa A. Olivetti ◽  
Jacqueline M. Cole ◽  
Edward Kim ◽  
Olga Kononova ◽  
Gerbrand Ceder ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Data Driven ◽  
Materials Research

Materials informatics: Facilitating the integration of data-driven materials research with education

JOM ◽  
2008 ◽  
Vol 60 (3) ◽  
pp. 51-52 ◽  
Author(s):  
L. M. Bartolo ◽  
S. C. Glotzer ◽  
C. S. Lowe ◽  
A. C. Powell ◽  
D. R. Sadoway ◽  
...  
Keyword(s):  
Data Driven ◽  
Materials Informatics ◽  
Materials Research

scholarly journals Data-Driven Studies of Li-Ion-Battery Materials

Crystals ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 54 ◽  
Author(s):  
Steven Kauwe ◽  
Trevor Rhone ◽  
Taylor Sparks
Keyword(s):  
Materials Science ◽  
Modern Society ◽  
Research Process ◽  
Data Driven ◽  
Learning Tools ◽  
Materials Development ◽  
Battery Materials ◽  
Battery Systems ◽  
Materials Research ◽  
Specific Discharge

Batteries are a critical component of modern society. The growing demand for new battery materials—coupled with a historically long materials development time—highlights the need for advances in battery materials development. Understanding battery systems has been frustratingly slow for the materials science community. In particular, the discovery of more abundant battery materials has been difficult. In this paper, we describe how machine learning tools can be exploited to predict the properties of battery materials. In particular, we report the challenges associated with a data-driven investigation of battery systems. Using a dataset of cathode materials and various statistical models, we predicted the specific discharge capacity at 25 cycles. We discuss the present limitations of this approach and propose a paradigm shift in the materials research process that would better allow data-driven approaches to excel in aiding the discovery of battery materials.

High-voltage Electron Microscopy: Where it is and where it may be going

1995 ◽  
Vol 53 ◽  
pp. 80-81
Author(s):  
Charles W. Allen
Keyword(s):  
Electron Microscopy ◽  
High Voltage ◽  
Construction Cost ◽  
Research Community ◽  
High Voltage Electron Microscopy ◽  
Voltage Electron ◽  
High Voltage Electron ◽  
Materials Research

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.

Sign in / Sign up

Export Citation Format

Share Document