What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Electron
- Artificial intelligence
Nanotechnology, Crystal structure, Throughput, Binary number and Materials design are his primary areas of study.
The Nanotechnology study combines topics in areas such as Quantum computer, Electronic properties, Topological insulator and Topology.
His work deals with themes such as Chemical physics, Oxide, Mixed oxide, Ab initio and Thermodynamic model, which intersect with Crystal structure.
His Ab initio study combines topics in areas such as Transition metal, Thermodynamics, Quantum, Crystal structure prediction and Tetragonal crystal system.
His Binary number research includes elements of Data mining and Compressed sensing.
His Structure research is multidisciplinary, relying on both Interface, Complement and Computational science.
His most cited work include:
- The high-throughput highway to computational materials design (1011 citations)
- High-throughput electronic band structure calculations: challenges and tools (680 citations)
- AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations (482 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Thermodynamics, Condensed matter physics, Ab initio, Binary number and Throughput.
Stefano Curtarolo combines subjects such as Transition metal, Phase, Phase diagram and Adsorption with his study of Thermodynamics.
His Condensed matter physics research integrates issues from Semiconductor and Density functional theory.
His Ab initio research focuses on Crystal structure and how it relates to Chemical physics.
Stefano Curtarolo has included themes like Structure, Ab initio quantum chemistry methods and Ternary operation in his Binary number study.
Stefano Curtarolo conducted interdisciplinary study in his works that combined Throughput and Nanotechnology.
He most often published in these fields:
- Thermodynamics (20.15%)
- Condensed matter physics (19.15%)
- Ab initio (19.90%)
What were the highlights of his more recent work (between 2018-2021)?
- Condensed matter physics (19.15%)
- Enthalpy (6.97%)
- Density functional theory (6.97%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Condensed matter physics, Enthalpy, Density functional theory, Thermodynamics and Materials design.
His studies in Condensed matter physics integrate themes in fields like Group, Dielectric and Constant.
His research in Enthalpy intersects with topics in Python, Lonsdaleite, Vickers hardness test, Ab initio and Statistical physics.
Stefano Curtarolo has researched Ab initio in several fields, including Characterization and Phonon.
His Statistical physics research incorporates themes from Thermal expansion, Bulk modulus and Taylor series.
The concepts of his Thermodynamics study are interwoven with issues in Alloy, Line and Ternary operation.
Between 2018 and 2021, his most popular works were:
- Phase stability and mechanical properties of novel high entropy transition metal carbides (145 citations)
- High-entropy ceramics (127 citations)
- The 2019 materials by design roadmap (122 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Electron
- Artificial intelligence
Stefano Curtarolo mostly deals with Density functional theory, Crystal, Enthalpy, Artificial intelligence and Ceramic.
His Density functional theory research is multidisciplinary, incorporating perspectives in Condensed matter physics, Doping, Spin-½, Hall effect and Semiconductor.
His study in Enthalpy is interdisciplinary in nature, drawing from both Local-density approximation, Debye model, k-nearest neighbors algorithm, Atom and Ab initio.
His studies deal with areas such as Thermodynamics and Ternary operation as well as Local-density approximation.
The study incorporates disciplines such as Spark plasma sintering, Carbide, Nanoindentation, Transition metal and High entropy alloys in addition to Thermodynamics.
His Artificial intelligence research includes themes of Machine learning, Energy and Pattern recognition.
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