6 papers
HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy
Julius Riel, Vishwa Mohan Singh, Sai Anirudh Aryasomayajula +10
Hierarchical structure is common in image data, where fine-grained clusters often merge into larger, coarser semantic groups. In biological cell images, current self-supervised lea…
A Synthesizability-Guided Pipeline for Materials Discovery
Thorben Prein, Willis O'Leary, Aikaterini Flessa Savvidou +2
Computational materials discovery relies on the generation of plausible crystal structures. The plausibility is typically judged through density functional theory methods which, wh…
DiffSyn: A Generative Diffusion Approach to Materials Synthesis Planning
Elton Pan, Soonhyoung Kwon, Sulin Liu +9
The synthesis of crystalline materials, such as zeolites, remains a significant challenge due to a high-dimensional synthesis space, intricate structure-synthesis relationships and…
Regress, Don't Guess -- A Regression-like Loss on Number Tokens for Language Models
Jonas Zausinger, Lars Pennig, Anamarija Kozina +13
While language models have exceptional capabilities at text generation, they lack a natural inductive bias for emitting numbers and thus struggle in tasks involving quantitative re…
Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials
Thorben Prein, Elton Pan, Janik Jehkul +3
Inorganic synthesis planning currently relies primarily on heuristic approaches or machine-learning models trained on limited datasets, which constrains its generality. We demonstr…
Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning
Thorben Prein, Elton Pan, Sami Haddouti +8
Retrosynthesis strategically plans the synthesis of a chemical target compound from simpler, readily available precursor compounds. This process is critical for synthesizing novel…