Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances
Top Cited Papers
Open Access
- 6 March 2024
- journal article
- research article
- Published by Taylor & Francis in Science and Technology of Advanced Materials: Methods
- Vol. 4 (1)
Abstract
No abstract availableKeywords
Funding Information
- Austrian Science Fund (P32847-N)
- European Union Horizon 2020 (Medical Device Obligation Taskforce MDOT)
This publication has 247 references indexed in Scilit:
- Accelerating materials property predictions using machine learningScientific Reports, 2013
- Ionic liquids for the synthesis of metal nanoparticlesPhysica Status Solidi (b), 2013
- The high-throughput highway to computational materials designNature Materials, 2013
- Ultra‐thin anodic alumina capacitor films for plastic electronicsPhysica Status Solidi (a), 2012
- High-throughput characterization of mechanical properties of Ti–Ni–Cu shape memory thin films at elevated temperatureMaterials Science and Engineering: A, 2011
- Combinatorial synthesis of chemically diverse core-shell nanoparticles for intracellular deliveryProceedings of the National Academy of Sciences, 2011
- Combinatorial and High-Throughput Screening of Materials Libraries: Review of State of the ArtACS Combinatorial Science, 2011
- Ab initio molecular simulations with numeric atom-centered orbitalsComputer Physics Communications, 2009
- Crystallography Open Database – an open-access collection of crystal structuresJournal of Applied Crystallography, 2009
- Rapid structural mapping of ternary metallic alloy systems using the combinatorial approach and cluster analysisReview of Scientific Instruments, 2007