Fuels & Chemicals
Make fuels and chemicals from domestic feedstocks and CO₂. Discover catalysts faster, optimize reaction conditions systematically, engineer selectivity with confidence, and design reliable pathways from lab to plant. Investigate, predict, and deploy new chemical processes and reaction modalities. Alchemic brings evidence-backed intelligence to every stage of fuels and chemicals R&D.
Key Workflows
Fuels and chemicals programs demand rapid iteration across vast chemical spaces, from the catalyst to the plant. Alchemic structures the search, reduces redundant experiments, and preserves institutional knowledge.
Catalyst Screening and Discovery
Search vast design spaces of metal complexes, heterogeneous catalysts, enzymes, or organocatalysts guided by foundation models. Alchemic scores candidates against activity, selectivity, stability, cost, and availability, then recommends the most informative experiments.
Reaction Condition Optimization
Systematically explore temperature, pressure, solvent, concentration, and catalyst loading to maximize yield and selectivity. Model-guided experimental design reduces the number of runs needed to reach optimal conditions.
Selectivity Engineering
Navigate the relationship between catalyst structure and product selectivity. Alchemic retrieves evidence from literature and internal data to identify structural motifs, ligand effects, and condition windows that control selectivity outcomes.
Catalyst Lifetime Prediction
Anticipate deactivation, poisoning, and fouling before long-duration runs. Alchemic connects accelerated test data, mechanistic hypotheses, and historical campaign results to forecast catalyst lifetime under operating conditions.
Scale-up Pathway Design
Translate lab-scale catalytic performance into process-scale reality. Assess heat and mass transfer effects, catalyst form factor requirements, regeneration strategies, and equipment constraints before committing to pilot runs.
Domestic Feedstock and Critical Building Block Production
Identify and optimize catalytic processes that leverage domestically available resources for large-scale critical chemical building blocks. Alchemic evaluates alternative feedstocks, novel reaction modalities, and supply chain resilience to reduce dependency on imported intermediates.
How Alchemic Helps
Making fuels and chemicals sits at the intersection of molecular design, reaction engineering, and process design. Alchemic connects these dimensions in a single platform.
Reaction Knowledge Graph
Unify reaction data from literature, patents, ELNs, and internal databases. Search by substrate, catalyst, product, condition, or outcome with full provenance and citations.
Structure-Activity Reasoning
Foundation models connect molecular structure, electronic properties, and reaction outcomes to help teams identify why catalysts work and how to improve them.
Experimental Prioritization
Not all experiments are equally informative. Alchemic agents rank proposed experiments by expected information gain, helping teams allocate limited lab resources to the highest-value runs.
Closed-Loop Campaigns
Run iterative design-test-learn cycles where each result updates the model and informs the next round. Every campaign builds institutional knowledge that transfers to future programs.
New Reaction Modalities
Explore and validate emerging reaction modalities, from electrochemistry and photocatalysis to mechanochemistry and flow chemistry, with AI-guided experimental design that identifies the most promising conditions faster.
Inside a campaign: Heterogeneous catalyst for selectivity
Lift selectivity on a hydrogenation step without giving up yield.
A process chemist needs higher selectivity in a key hydrogenation step. The current catalyst works, but generates a stubborn over-reduction byproduct that costs downstream purification. Cost and supplier availability rule out exotic noble-metal swaps.
- Step 01 · Input
Frame the optimization
Substrate, baseline catalyst, current selectivity and conversion, hazard envelope, and a $/kg ceiling are entered as the optimization frame.
- Step 02 · Generation
Generate combinations
The agent retrieves prior art, then proposes ~25 metal–support–condition combinations with predicted selectivity, conversion, and leaching risk.
- Step 03 · Tradeoffs
Map the Pareto frontier
6 candidates dominate the baseline on selectivity without sacrificing conversion. The agent flags two with limited public data, so proceed with caution.
- Step 04 · Plan
Plan the experiments
A design-of-experiments protocol covering temperature, pressure, and substrate loading is staged for the top 3. Results return to the loop and update predictions.
Outcome: Instead of a months-long catalyst hunt, the team arrives at bench with three credible candidates, calibrated expectations on each, and a sharper view of which conditions matter most for the next campaign.
Accelerate your fuels and chemicals programs
See how Alchemic helps teams find better catalysts, conditions, and pathways to production with fewer experiments.