Andres Ortega‐Guerrero

Materials Chemistry · 2024

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 15 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Computational Chemistry & Drug Discovery — 7 citations into it

Whom Andres Ortega‐Guerrero trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Peter Ertl

Papers from Andres Ortega‐Guerrero's perspective

Ordered by how strongly the researchers Andres Ortega‐Guerrero trusts endorse each paper. Same corpus, a different order for every researcher.

1. Language Models are Few-Shot Learners (Neural Information Processing Systems)
2. The Pile: An 800GB Dataset of Diverse Text for Language Modeling
3. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
4. Unsupervised word embeddings capture latent knowledge from materials science literature
5. GuacaMol: Benchmarking Models for De Novo Molecular Design
6. Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction (ACS Central Science)
7. GFN2-xTB-An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions.
8. Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers (Annual Meeting of the Association for Computational Linguistics)
9. Scaling tree-based automated machine learning to biomedical big data with a feature set selector (Bioinform.)
10. Long Short-Term Memory

Their papers

• Leveraging large language models for predictive chemistry (2024) — 403 citations