TY  - CONF
A1  - George, Janine
T1  - High-throughput Computational Screening With Chemical Heuristics, Workflows, and Machine Learning
N2  - Within my talk, I have introduced the audience to high-throughput materials discovery with the help of workflow tools, machine learning and chemical heuristics. I started with a general introduction to the Materials Project software infrastructure and databases such as the Materials Project and NOMAD. Then, I dived into machine learning tasks relying on chemical bonding information, machine-learned interatomic potentials, and experimental data. Target properties of the machine-learning tasks were thermal properties, phonons, magnetism and synthesizability.
T2  - Seminar at the Inorganic Chemistry Laboratory in Oxford
CY  - Oxford, United Kingdom
DA  - 25.09.2025
KW  - High-throughput
KW  - Sustainability
KW  - Machine Learning
KW  - Materials Design
KW  - Thermal Conductivity
KW  - Magnetism
KW  - Synthesizability
KW  - Materials Acceleration Platform
PY  - 2025
AN  - OPUS4-64217
LA  - eng
AD  - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER  -