{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-scaling-deep-learning-for-materials-discovery-gnome","slug":"scaling-deep-learning-for-materials-discovery-gnome","name":"Scaling deep learning for materials discovery (GNoME)","type":"product","url":"https://www.nature.com/articles/s41586-023-06735-9","page_url":"https://unfragile.ai/scaling-deep-learning-for-materials-discovery-gnome","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-scaling-deep-learning-for-materials-discovery-gnome__cap_0","uri":"capability://data.processing.analysis.graph.neural.network.based.crystal.structure.prediction","name":"graph neural network-based crystal structure prediction","description":"Predicts stable crystal structures and their properties using graph neural networks (GNNs) that represent atomic arrangements as graphs where nodes are atoms and edges encode spatial relationships. The model learns to predict formation energy, stability, and material properties by processing the topological and geometric features of crystal lattices, enabling discovery of novel stable materials without expensive quantum mechanical simulations.","intents":["Predict whether a hypothetical crystal composition will be thermodynamically stable before synthesis","Screen millions of candidate materials to identify promising candidates for experimental validation","Estimate key material properties (band gap, conductivity, mechanical strength) from structure alone","Accelerate materials discovery pipelines by replacing or augmenting DFT calculations"],"best_for":["Materials scientists and chemists automating high-throughput screening workflows","Research teams with limited access to high-performance computing for DFT","Drug discovery teams seeking novel antibiotic scaffolds with explainable predictions"],"limitations":["Predictions are probabilistic and require experimental validation; model confidence varies by material class","Training data biased toward well-studied material families; performance degrades for out-of-distribution compositions","Graph representation assumes periodic crystal structures; amorphous or disordered materials not supported","Computational cost scales with system size; very large unit cells (>100 atoms) may exceed practical inference budgets"],"requires":["Atomic structure data in standard formats (CIF, POSCAR, or similar)","Computational resources for inference (GPU recommended for batch processing)","Understanding of crystal chemistry and stability metrics for result interpretation"],"input_types":["crystal structure files (CIF, POSCAR, XYZ)","chemical composition (elemental formula)","lattice parameters and atomic coordinates"],"output_types":["formation energy predictions (eV/atom)","stability classification (stable/metastable/unstable)","material property estimates (band gap, elastic constants)","confidence scores and uncertainty quantification"],"categories":["data-processing-analysis","materials-science","deep-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-deep-learning-for-materials-discovery-gnome__cap_1","uri":"capability://planning.reasoning.active.learning.driven.materials.exploration.with.uncertainty.quantification","name":"active learning-driven materials exploration with uncertainty quantification","description":"Implements an active learning loop that iteratively selects the most informative candidate materials to evaluate experimentally or computationally, using model uncertainty (ensemble disagreement, Bayesian posterior variance) to prioritize exploration of underexplored regions of composition space. The system balances exploitation (high predicted performance) with exploration (high uncertainty) to maximize discovery efficiency with limited experimental budget.","intents":["Identify which candidate materials to synthesize next to maximize learning per experiment","Quantify model confidence in predictions and flag regions requiring more data","Reduce experimental iterations needed to discover high-performance materials","Adaptively refine the model as new experimental results are collected"],"best_for":["Research teams with constrained experimental budgets seeking maximum discovery ROI","Materials discovery projects where each experiment is expensive (synthesis, characterization)","Iterative workflows where model retraining between experimental batches is feasible"],"limitations":["Requires ground-truth labels from experiments or high-fidelity simulations to close the loop; cold-start problem with limited initial data","Uncertainty estimates depend on model architecture; ensemble methods add computational overhead","May converge to local optima if acquisition function is poorly calibrated","Assumes experimental feedback is reliable and timely; delays in validation slow learning"],"requires":["Initial training dataset of 100+ materials with experimental or computed properties","Mechanism to evaluate selected candidates (DFT, experiments, or hybrid)","Computational infrastructure for model retraining and inference in feedback loops"],"input_types":["candidate material compositions or structures","experimental or computational validation results","uncertainty quantification from model ensemble"],"output_types":["ranked list of next candidates to evaluate (sorted by acquisition function)","uncertainty estimates per candidate","updated model weights after incorporating new data"],"categories":["planning-reasoning","data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-deep-learning-for-materials-discovery-gnome__cap_2","uri":"capability://planning.reasoning.explainable.property.attribution.for.discovered.materials","name":"explainable property attribution for discovered materials","description":"Provides interpretable explanations for material property predictions by identifying which atomic features, local chemical environments, and structural motifs most strongly influence the model's output. Uses attention mechanisms, feature importance analysis, and local surrogate models to decompose black-box GNN predictions into human-understandable chemical insights, enabling chemists to validate predictions and guide synthesis strategies.","intents":["Understand why the model predicts a material will be stable or have specific properties","Identify key structural features or chemical motifs driving material performance","Validate model predictions against chemical intuition before committing to synthesis","Guide rational design of next-generation materials by understanding feature importance"],"best_for":["Chemists and materials scientists who need to trust and validate AI predictions","Research teams publishing discoveries and requiring mechanistic explanations for peer review","Iterative design workflows where understanding failure modes informs next experiments"],"limitations":["Explanations are post-hoc approximations; may not fully capture complex non-linear interactions in the model","Attention weights or feature importance scores can be misleading if model relies on spurious correlations","Explanations are local to individual predictions; global model behavior may differ","Requires domain expertise to interpret chemical significance of identified features"],"requires":["Access to model internals (attention weights, embeddings, or gradients)","Chemical domain knowledge to interpret identified features and motifs","Visualization tools for atomic-level feature importance"],"input_types":["crystal structure of predicted material","model prediction and confidence score","atomic coordinates and chemical identities"],"output_types":["per-atom importance scores","identified structural motifs or chemical environments","attention visualizations highlighting influential atoms","natural language explanations of key drivers"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-deep-learning-for-materials-discovery-gnome__cap_3","uri":"capability://planning.reasoning.multi.property.optimization.and.pareto.frontier.discovery","name":"multi-property optimization and pareto frontier discovery","description":"Simultaneously optimizes multiple competing material properties (e.g., stability, conductivity, mechanical strength) to identify Pareto-optimal materials where no single property can be improved without sacrificing another. Uses multi-objective optimization algorithms (e.g., evolutionary algorithms, Bayesian multi-objective optimization) to explore the trade-off surface and surface promising candidates across different performance profiles.","intents":["Find materials that balance multiple conflicting requirements (e.g., high strength AND low cost)","Understand trade-offs between material properties to guide design decisions","Identify diverse candidates across the Pareto frontier rather than a single optimum","Optimize for application-specific property combinations (e.g., antibiotic efficacy vs toxicity)"],"best_for":["Materials engineers designing for real-world applications with multiple constraints","Drug discovery teams optimizing for efficacy, toxicity, and synthesizability simultaneously","Research teams exploring fundamental trade-offs in material design"],"limitations":["Computational cost scales exponentially with number of objectives; 3-5 properties practical, >10 becomes intractable","Pareto frontier may be discontinuous or have many local optima; global optimization not guaranteed","Requires accurate predictions for all objectives; errors in any property prediction distort the frontier","Trade-off analysis assumes all properties are equally weighted; user must specify preference weights"],"requires":["Trained models for each material property to optimize","Definition of property ranges and optimization direction (maximize/minimize)","Computational budget for multi-objective search (typically 1000s-10000s of evaluations)"],"input_types":["candidate material compositions or structures","property prediction models for each objective","weights or preference functions for objectives"],"output_types":["Pareto frontier of non-dominated solutions","ranked candidates by hypervolume or other multi-objective metrics","trade-off visualizations (2D/3D scatter plots of property space)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-deep-learning-for-materials-discovery-gnome__cap_4","uri":"capability://automation.workflow.large.scale.composition.space.screening.with.scalable.inference","name":"large-scale composition space screening with scalable inference","description":"Performs high-throughput screening across millions of candidate material compositions by leveraging efficient GNN inference on GPUs and distributed computing. Processes compositions in batches, caches embeddings for related materials, and uses approximate nearest-neighbor search to identify similar materials and avoid redundant evaluations, enabling exploration of vast compositional spaces in hours rather than weeks.","intents":["Screen millions of candidate compositions to identify top performers for experimental validation","Rapidly explore new compositional regions after discovering a promising material class","Identify similar materials to known high-performers using embedding similarity","Generate comprehensive property maps across composition space for visualization and analysis"],"best_for":["High-throughput materials discovery pipelines with large candidate pools","Teams with access to GPU clusters or cloud computing for batch inference","Exploratory research phases where broad screening precedes focused optimization"],"limitations":["Inference latency and memory scale with candidate pool size; 10M+ compositions requires distributed infrastructure","Batch processing introduces latency; real-time single-prediction queries may be slower than optimized inference servers","Caching strategies assume compositional similarity; may miss distant but promising materials","Approximate nearest-neighbor search trades accuracy for speed; rare materials may be overlooked"],"requires":["GPU infrastructure (NVIDIA A100 or equivalent) for efficient batch inference","Distributed computing framework (Ray, Spark) for processing 1M+ candidates","Sufficient memory for embedding caches (typically 10-100 GB for large screening)"],"input_types":["list of candidate compositions (elemental formulas or structure files)","optional: constraints on composition (e.g., exclude toxic elements)"],"output_types":["ranked list of top candidates with predicted properties","property distributions across composition space","embedding vectors for similarity-based clustering"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-deep-learning-for-materials-discovery-gnome__cap_5","uri":"capability://planning.reasoning.transfer.learning.across.material.classes.and.property.domains","name":"transfer learning across material classes and property domains","description":"Leverages pre-trained GNN models learned on diverse material families and properties to accelerate learning on new, data-scarce material classes. Uses domain adaptation techniques (fine-tuning, feature alignment) to transfer learned representations of atomic bonding patterns and structural stability from well-studied materials (e.g., oxides, metals) to novel classes (e.g., organic frameworks, halide perovskites), reducing data requirements for new applications.","intents":["Quickly build predictive models for novel material classes with limited experimental data","Leverage knowledge from related material families to improve predictions on new targets","Reduce training data requirements by 10-100x through transfer learning","Adapt pre-trained models to new properties (e.g., from stability to conductivity) with minimal retraining"],"best_for":["Research teams exploring emerging material classes (e.g., new perovskites, MOFs) with sparse data","Materials discovery projects with tight timelines and limited experimental budgets","Cross-domain applications where source and target materials share structural similarities"],"limitations":["Transfer learning effectiveness depends on similarity between source and target domains; distant material classes may not benefit","Fine-tuning can lead to overfitting on small target datasets; requires careful regularization","Negative transfer possible if source domain knowledge conflicts with target domain","Requires access to pre-trained models; not all material classes have available pre-training"],"requires":["Pre-trained GNN model on related material class or property","Target dataset of 50-500 materials with experimental or computed properties","Computational resources for fine-tuning (GPU, typically hours to days)"],"input_types":["pre-trained model weights","target material structures and properties","optional: domain adaptation parameters (learning rate, regularization)"],"output_types":["fine-tuned model weights for target domain","performance metrics on target dataset","transfer learning effectiveness analysis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-deep-learning-for-materials-discovery-gnome__cap_6","uri":"capability://automation.workflow.integration.with.experimental.validation.pipelines.and.feedback.loops","name":"integration with experimental validation pipelines and feedback loops","description":"Connects AI predictions to automated or semi-automated experimental workflows, enabling closed-loop discovery where predicted materials are synthesized, characterized, and results fed back to retrain the model. Manages data flow between prediction, experimental design, lab automation, and model retraining, with APIs for integration with robotic synthesis platforms, characterization instruments, and LIMS systems.","intents":["Automatically select next materials to synthesize based on model predictions and uncertainty","Capture experimental results and integrate them into model retraining pipelines","Track provenance of discovered materials from prediction through validation","Coordinate between computational and experimental teams in iterative discovery workflows"],"best_for":["Research institutions with integrated computational and experimental capabilities","Teams using robotic synthesis platforms or automated characterization instruments","Discovery projects with sufficient budget for iterative experimental validation"],"limitations":["Integration complexity scales with number of experimental platforms; each instrument requires custom adapter","Feedback loop latency depends on experimental turnaround time; slow experiments limit learning speed","Experimental noise and measurement errors can corrupt model retraining if not properly handled","Requires standardized data formats and APIs; legacy instruments may need custom middleware"],"requires":["APIs or middleware for experimental platforms (robotic synthesis, characterization instruments)","LIMS or experiment tracking system for data management","Data validation and quality control procedures for experimental results","Computational infrastructure for model retraining between experimental batches"],"input_types":["predicted material candidates with properties and uncertainty","experimental results (synthesis success, measured properties, characterization data)","experimental constraints (available elements, synthesis methods, timelines)"],"output_types":["experimental design recommendations (next materials to synthesize)","updated model weights after incorporating experimental data","discovery reports with validated materials and mechanistic insights"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-deep-learning-for-materials-discovery-gnome__cap_7","uri":"capability://planning.reasoning.structure.property.relationship.mining.and.chemical.rule.extraction","name":"structure-property relationship mining and chemical rule extraction","description":"Analyzes learned GNN representations and predictions to extract interpretable chemical rules and structure-property relationships (e.g., 'materials with this local coordination environment tend to be stable'). Uses clustering, decision trees, and symbolic regression on model embeddings to identify recurring patterns and generate human-readable rules that explain material behavior and guide rational design.","intents":["Extract chemical design principles from AI predictions to guide rational materials design","Identify recurring structural motifs associated with high performance","Generate testable hypotheses about structure-property relationships","Communicate AI discoveries to chemists in familiar chemical language"],"best_for":["Materials scientists seeking mechanistic understanding of discovered materials","Research teams publishing discoveries and requiring chemical explanations","Educational contexts where understanding design principles is as important as discovery"],"limitations":["Extracted rules are approximations; may miss complex non-linear interactions captured by the neural network","Rule extraction is computationally expensive; requires analyzing thousands of predictions and embeddings","Rules are specific to training data distribution; may not generalize to out-of-distribution materials","Symbolic regression can produce overly complex or unintuitive rules; human curation required"],"requires":["Access to model embeddings and intermediate representations","Large set of predictions (1000s) for statistical analysis","Domain expertise to interpret and validate extracted rules"],"input_types":["GNN embeddings for materials","predicted properties and experimental validation results","crystal structures and chemical compositions"],"output_types":["extracted chemical rules (e.g., decision trees, symbolic equations)","identified structural motifs and their property correlations","rule confidence and applicability domain"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaling-deep-learning-for-materials-discovery-gnome__cap_8","uri":"capability://data.processing.analysis.composition.constrained.materials.discovery.with.element.restrictions","name":"composition-constrained materials discovery with element restrictions","description":"Restricts materials discovery to specific elemental subsets based on availability, cost, toxicity, or environmental constraints. Enables targeted screening within allowed composition spaces (e.g., 'find stable materials using only Earth-abundant elements' or 'exclude toxic heavy metals'). Implements efficient filtering and composition-space partitioning to avoid evaluating forbidden compositions.","intents":["Discover materials using only Earth-abundant or low-cost elements","Find alternatives to toxic or rare elements in existing high-performance materials","Optimize for sustainability by excluding environmentally harmful elements","Accelerate screening by restricting search to chemically relevant composition spaces"],"best_for":["Materials discovery for sustainable or green applications","Industrial research teams optimizing for cost and supply chain stability","Drug discovery projects optimizing for biocompatibility and low toxicity"],"limitations":["Restricting composition space may exclude high-performance materials; trade-off between constraints and performance","Element restrictions are binary; no support for soft constraints (e.g., 'prefer abundant elements')","Constraint propagation can be computationally expensive for complex restrictions","May miss novel materials that require 'forbidden' elements in small quantities"],"requires":["Definition of allowed elements and composition constraints","Optional: cost or toxicity data for elements","Efficient filtering mechanism to avoid evaluating forbidden compositions"],"input_types":["list of allowed elements","optional: element costs, toxicity scores, or availability data","composition constraints (e.g., max concentration of specific elements)"],"output_types":["filtered candidate list respecting composition constraints","property predictions for allowed compositions only","analysis of performance trade-offs due to constraints"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["Atomic structure data in standard formats (CIF, POSCAR, or similar)","Computational resources for inference (GPU recommended for batch processing)","Understanding of crystal chemistry and stability metrics for result interpretation","Initial training dataset of 100+ materials with experimental or computed properties","Mechanism to evaluate selected candidates (DFT, experiments, or hybrid)","Computational infrastructure for model retraining and inference in feedback loops","Access to model internals (attention weights, embeddings, or gradients)","Chemical domain knowledge to interpret identified features and motifs","Visualization tools for atomic-level feature importance","Trained models for each material property to optimize"],"failure_modes":["Predictions are probabilistic and require experimental validation; 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