SOURCE-LINKED INTELLIGENCE
Universal Multireference Diagnostics for High-Throughput Screening of Large Molecules and Solids
optical properties, and reaction mechanisms to drug discovery in pharmaceutical industries and materials design in supramolecular industries. Its importance has only increased with the resurgence of machine learning and the advent of artificial intelligence. Despite the widespread use of DFT, current density functional approximations can capture well the so-called nondynamic electron correlation, severely compromising the simulations whenever these multireference (MR) effects comes into play. Hence, the development of correlation diagnostics capable of identifying MR systems is critical for computational chemistry. Such diagnostics are valuable not only for high-throughput screening but also for individual molecular studies, where accurate simulation depends on the selection of an appropriate computational method. This need is even more evident in periodic systems, where technologically critical properties –such as magnetic behaviour, optical responses, and metal–insulator transitions– depend sensitively on its accurate treatment. UniMRD aims to develop universal MR diagnostics c
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 194074.56
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T04:21:15.460Z. This is not the publication date.