For centuries, discovering a new material took decades of lab work. Alchemist is our gateway to NVIDIA ALCHEMI: atomistic simulation with near-quantum-chemistry accuracy at GPU speed, running on-premise on a DGX Spark. Thousands of molecular candidates evaluated before touching a single flask.
Computational chemistry has always lived a war between accuracy and speed: DFT is exact but painfully slow; classical force fields are fast but imprecise. ALCHEMI breaks that trade-off with GPU-accelerated machine-learning interatomic potentials (MLIPs), in three integrated layers:
Containerized microservices, ready to orchestrate. MLIP inference as an API: send structures, get energies, forces and properties back. Ideal for plugging into a client's existing pipelines.
GPU-first Python framework: nvalchemi-toolkit. Batched molecular dynamics, massive structural relaxation, composable multi-stage pipelines, and support for MACE, AIMNet2 or your own model.
High-performance primitives on NVIDIA Warp: neighbor lists, Ewald/PME electrostatics, dispersion. Scales past 100,000 atoms with differentiable PyTorch and JAX bindings.
Each case is a screening that today takes months of lab work or supercomputing clusters — and in this PoC runs in hours, on a machine that fits on your desk.
Batched screening of thousands of electrolyte formulations, SEI additives and selective sorbents to extract lithium from brine without evaporation ponds. The natural case for Chile, heart of the lithium triangle.
Non-metallic mining · EnergyDesign of selective collectors and less toxic leaching agents by simulating the mineral–reagent interface at atomic scale. Higher recovery with lower environmental impact.
Large-scale miningMassive search for low-cost OER/HER catalysts for electrolysis. Iridium costs more than gold; finding its replacement defines the economics of green hydrogen in Magallanes and Antofagasta.
Energy · Green H2AI-designed porous materials to desalinate seawater and treat mining effluents with lower energy use. Simulation of ion transport through candidate membranes.
Water resourcesDiscovery of next-generation dielectric coolants — the same case NVIDIA showcased at SC25. If you operate or sell AI infrastructure, this one hits home.
Datacenter · AI infrastructureEmollients, UV filters, formulation stability and ingredient compatibility — one of ALCHEMI's official use cases, with shorter sales cycles than heavy industry.
Retail · Consumer goodsA live, reproducible demo running 100% on-premise on the DGX Spark. No cloud, and not a single molecular structure leaves the room.
We generate or load thousands of candidate structures (electrolytes, catalysts, sorbents) as GPU-resident atomic graphs.
Structural relaxation and molecular dynamics of thousands of systems in parallel with MLIPs (MACE / AIMNet2) on the ALCHEMI Toolkit — near-DFT accuracy at a fraction of the cost.
Ranking by energy, stability and target properties, with live interactive molecular visualization. The client sees their candidate shortlist by the end of the session.
# DGX Spark · GB10 · aarch64 · CUDA 13 $ uv pip install nvalchemi-toolkit $ python run_screening.py --candidates 5000 \ --model mace-mp --stage relax+md [alchemist] loading 5,000 structures → GPU [alchemist] batch MD · 300K · 10ps · NVT ████████████████████░░ 91% ETA 00:04:12 ✓ 5,000 systems relaxed in 38 min (estimated DFT equivalent: ~14 months) ★ top-10 candidates → results/shortlist.zarr $ ▊
Alchemists had the athanor — the furnace where transmutation happened. Ours is an NVIDIA DGX Spark: the Grace Blackwell GB10 superchip with unified memory, in a form factor that installs in the client's office, not in a datacenter.
That means full data sovereignty: formulas, structures and R&D results — the most sensitive asset of a chemical or mining company — never leave the perimeter. And when screening demands more scale, the same pipeline moves unchanged to an H200 cluster or DGX Cloud.
A 90-minute discovery session with our team: we define your chemical space, run a real screening on the DGX Spark, and you leave with the shortlist.
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