Active Learning (Machine Learning)
A training strategy where the model identifies which unlabeled examples would be most informative to label next, reducing the amount of labeled data needed to reach good performance.
Glossary
Plain-language definitions for AI terms, research methodology jargon, and model names used across the site.
A
D
De novo design
Generating entirely new molecular structures from scratch to meet a set of target properties — rather than selecting from existing compound libraries.
Density functional theory (DFT)
A quantum-mechanical computational method for calculating electronic structure and properties of atoms, molecules, and materials — the dominant method for generating training data for AI materials and chemistry models.
Diffusion Model
A class of generative AI model that learns to create new data (images, proteins, molecules, weather states) by learning to reverse a gradual noising process — the basis of tools like Stable Diffusion, RFdiffusion, and GenCast.
E
Embedding
A numerical vector representation of an object (a word, protein, molecule, or document) that captures its meaning or properties in a form AI models can work with — objects that are semantically similar tend to have similar embeddings.
Ensemble Forecasting
Running many slightly different versions of a forecast model to produce a range of possible outcomes rather than a single prediction — the standard approach for quantifying forecast uncertainty in meteorology.
F
Federated Learning
A machine learning approach where a model is trained across multiple data-holding institutions without the raw data ever leaving each site — particularly important for medical research where patient data cannot be shared.
Fine-Tuning
Adapting a pre-trained AI model to a specific task or domain by continuing to train it on a smaller, task-specific dataset — rather than training a new model from scratch.
Formation energy
The energy change when a compound is formed from its constituent elements in their standard states — a key indicator of thermodynamic stability and the primary prediction target for AI materials discovery models.
Foundation Model
A large AI model trained on broad data at scale and designed to be adapted to many downstream tasks — GPT-4, ESM-2, and AlphaFold are all examples in different scientific domains.
G
Graph Neural Network (GNN)
A type of neural network designed to operate on graph-structured data — where entities (nodes) are connected by relationships (edges) — used in molecular modeling, weather forecasting, and materials discovery.
Gravitational wave
Ripples in spacetime produced by accelerating massive objects — detected by laser interferometers and increasingly analyzed with AI to extract source parameters from noisy signals.
H
Hallucination (AI)
When an AI model generates text that sounds confident and plausible but is factually incorrect — a fundamental limitation of large language models that matters enormously in research contexts.
L
Large Language Model (LLM)
An AI model trained on vast text corpora to predict and generate language — the technology behind ChatGPT, Claude, and Gemini, increasingly used in research for writing, summarization, and literature synthesis.
Latent Space
The compressed, continuous representation space a generative AI model uses internally — where similar molecules, proteins, or data points cluster together, and where interpolation and search can discover new candidates.
M
Molecular fingerprint
A fixed-length binary or count vector that encodes which structural features are present in a molecule — the standard way to convert chemical structures into numerical inputs for machine learning models.
N
Named entity recognition (NER)
An NLP task that identifies and classifies named things in text — people, organizations, places, dates, gene names, drug names — enabling structured extraction from unstructured documents.
Numerical Weather Prediction (NWP)
The traditional physics-based approach to weather forecasting, which solves mathematical equations representing atmospheric dynamics on a grid — the method AI weather models are increasingly being benchmarked against.
P
Prompt Engineering
The practice of designing inputs to an AI language model to reliably get better, more accurate, or more useful outputs — a practical skill for researchers using LLMs in their work.
Protein Language Model
An AI model trained on large databases of protein sequences — treating amino acids like words — to learn representations that capture evolutionary and structural information useful for property prediction and design.
Q
Qualitative data analysis (QDA)
The systematic interpretation of non-numerical data — interviews, field notes, documents, images — to identify patterns, themes, and meanings. AI tools can assist with coding and thematic analysis but require careful human oversight.
R
Reanalysis Data
A consistent historical record of atmospheric conditions produced by running a modern weather model over decades of past observations — the primary training data for AI weather models like GraphCast.
Retrieval-Augmented Generation (RAG)
An approach that grounds an LLM's responses in retrieved documents — the model searches a document set first, then generates its answer based on what it actually found, reducing hallucination.
Retrosynthesis
A planning strategy in organic chemistry that works backward from a target molecule to identify feasible synthetic routes — AI tools now automate the search for these routes using reaction databases.
S
Simulation-based inference
A class of machine learning methods that infer the parameters of a scientific model by learning from simulations rather than requiring a tractable likelihood function — particularly useful in physics and cosmology.
SMILES notation
A compact text format for encoding molecular structures as strings — widely used to represent chemical compounds in AI training data and tool inputs.
Structure-based drug design
Designing drug molecules by exploiting the 3D structure of a biological target — using shape and chemistry of the binding site to guide which compounds to synthesize or screen.
Structured Data Extraction
The process of systematically pulling specific, predefined data fields from a set of documents — in research contexts, used to populate tables comparing outcomes, methods, or sample characteristics across papers.
Survey data (astronomy)
Large-scale astronomical observations systematically covering large areas of sky — the primary data source for many AI astronomy applications, producing catalogs of billions of objects.
Symbolic regression
A machine learning technique that searches for mathematical equations that fit a dataset — producing interpretable formulas rather than black-box predictions.
T
Topic Modeling
An unsupervised machine learning method that discovers recurring themes in a text corpus — useful for exploring large collections of survey responses, interview transcripts, or documents.
Transfer Learning
Using knowledge a model learned from one task or dataset to improve performance on a different but related task — the principle behind why pre-trained models can be adapted with far less data than training from scratch.
Transformer
The neural network architecture behind most modern AI — including GPT-4, AlphaFold, and ESM-2 — built around an attention mechanism that lets the model weigh relationships between all parts of its input simultaneously.
V
Virtual screening
Computationally ranking large libraries of compounds against a biological target to identify candidates worth testing experimentally — shortlisting millions of molecules before spending resources in the lab.
Z
Zero-shot classification
Classifying text into categories without training examples — you provide label names in plain language, and the model applies them based on semantic understanding alone.