Aiichiro Nakano — Papers on Cortexa

arxivcs.LGcond-mat.mtrl-sciphysics.chem-phphysics.comp-ph2026-07-06

EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation

Samuel Sahel-Schackis, Ken-ichi Nomura, Aiichiro Nakano, Matthias F. Kling, Thomas Linker

Foundation machine learning force fields (MLFFs) such as MACE-MP-0 and UMA cover broad chemical space at near density functional theory (DFT) accuracy. However, they assume equilibrium ground-state physics and do not natively handle externally induced changes to the electronic st…

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crossrefMachine Learning and Knowledge Extraction2022-08-05

VLA-SMILES: Variable-Length-Array SMILES Descriptors in Neural Network-Based QSAR Modeling

Antonina L. Nazarova, Aiichiro Nakano

Machine learning represents a milestone in data-driven research, including material informatics, robotics, and computer-aided drug discovery. With the continuously growing virtual and synthetically available chemical space, efficient and robust quantitative structure–activity rel…

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