Media Kit
Press resources for journalists, analysts, and partners covering FluxMateria.
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Press resources for journalists, analysts, and partners covering FluxMateria.
FluxMateria is a computational screening platform for life sciences, materials science, and chemistry — built on a breakthrough physics kernel that is fundamentally different from both conventional simulation (DFT) and machine learning / AI. The platform uses a single deterministic engine with no training data to evaluate molecules, materials, and reaction systems with full traceability, reproducibility, and confidence indicators on every prediction.
One-sentence description
FluxMateria is a computational screening platform powered by a new physics kernel — not DFT, not AI — that evaluates molecules, materials, and reactions up to 3.6 million times faster than conventional methods from one deterministic engine.
| Company | FluxMateria |
| Website | fluxmateria.com |
| Founded by | Roberto Campus |
| Location | Olbia, Sardinia, Italy |
| Launch status | Research preview |
| Access model | Public demos (no signup), guided walkthroughs, pilot access |
| Core domains | Life sciences, materials science, chemistry |
| Core technology | Breakthrough physics kernel — not DFT, not AI/ML |
| Key differentiator | New physics kernel, no training data, confidence indicators on every prediction |
| Headline speed | Up to 3,600,000× faster than conventional DFT |
The most important thing to understand about FluxMateria is what it is — and what it is not.
DFT solves the Schrödinger equation numerically using approximations. It is rigorous but computationally expensive — often prohibitively so for large-scale screening. FluxMateria's kernel takes a different mathematical path entirely, deriving properties from a geometric framework rather than solving wave equations iteratively.
ML/AI models are trained on existing data and make predictions by interpolation. They are fast, but they are black boxes: they cannot explain their outputs, they fail on chemistry outside their training distribution, and they cannot tell you when they are guessing. FluxMateria has no training data. It generalizes to novel chemistry on day one.
The FLUX engine derives molecular, materials, and reaction properties from first principles using a novel geometric approach. The result is a deterministic engine that combines the interpretability and reproducibility of physics with the speed of AI — and provides explicit confidence indicators on every prediction.
All benchmark claims are published on the FluxMateria platform with test conditions and methodology.
Founder, FluxMateria
Roberto Campus is a serial entrepreneur and the founder of FluxMateria. Over a 25-year career in technology, Campus has built and scaled ventures across SaaS, AI, and enterprise software. He previously co-founded Blog Sparks Network, an audience aggregation platform he grew to 10 million monthly users and $1M ARR. He has also co-founded ventures in voice AI and led enterprise architecture modernization for a major European institution.
Campus combines this operational and product-scaling experience with deep technical roots in software engineering, computational physics, and computer graphics — a combination that led him to develop the FLUX physics kernel underpinning FluxMateria. He leads the company's scientific vision, computational architecture, and product direction, with a focus on building deterministic, physics-based screening tools that deliver speed, reproducibility, and interpretability for scientific R&D teams. He is based in Sardinia, Italy.
Short bio (for bylines and press mentions)
Roberto Campus is a serial entrepreneur and the founder of FluxMateria. He previously co-founded Blog Sparks Network, scaling it to 10M monthly users, and has held technology leadership roles spanning SaaS, AI, and enterprise architecture. He created the FLUX physics kernel and leads FluxMateria's scientific and product direction. He is based in Sardinia, Italy.
Attribution: Roberto Campus, Founder, FluxMateria
“We built a new physics kernel — not a faster version of DFT, and not another AI black box. Scientific teams should not have to choose between speed, interpretability, and reproducibility.”
“When the cost of asking a safety question drops to near zero, it changes when you ask it. ADMET profiling moves from the end of the pipeline to the beginning.”
“The real breakthrough is not just speed. It's that we can screen molecules, materials, and reactions from one deterministic engine — with confidence signals on every prediction — and get results you can trace, reproduce, and audit.”
“We publish our benchmarks not because we have to, but because in computational chemistry the only honest stance is: here’s what we got — check it yourself.”
“AI models are fast but they can't tell you when they're guessing. DFT is rigorous but too slow to screen at scale. We built something that is neither — a new physics kernel that gives you both.”
Ready-to-use company descriptions for articles, press mentions, and partner materials.
FluxMateria is a computational discovery platform for life sciences, materials science, and chemistry. Built on a breakthrough physics kernel — not conventional DFT, not machine learning — the platform evaluates molecular, materials, and reaction properties from a single deterministic engine, delivering speed, interpretability, traceability, and reproducibility.
FluxMateria is a computational screening platform for life sciences, materials science, and chemistry. The platform is built on a breakthrough physics kernel — fundamentally different from both DFT and AI/ML approaches — that evaluates molecular properties, materials properties, reaction mechanisms, and spectroscopy from one deterministic engine with no training data. This means it generalizes to novel chemistry on day one, every result is fully reproducible, and every prediction includes a confidence indicator. FluxMateria is currently available in research preview through public demos, guided walkthroughs, and pilot-access programs for scientific and industrial R&D teams.
FluxMateria is a computational discovery and screening platform serving life sciences, materials science, and chemistry from a single engine built on a breakthrough physics kernel. Rather than relying on conventional DFT (rigorous but slow) or machine learning (fast but opaque), FluxMateria evaluates molecular, materials, and reaction properties from first principles at speeds up to 3.6 million times faster than DFT — with no training data and full determinism. Capabilities include ADMET profiling at approximately 350 molecules per second, mechanism of action prediction across more than 10,000 targets, binding affinity estimation, band-gap prediction across more than 1,000 materials, reaction mechanism classification, spectroscopy, synthesis planning, and inverse search. Every prediction includes confidence indicators. Enterprise features include an API-first architecture with over 150 endpoints, audit trails, role-based access, and organization-level data isolation. FluxMateria is currently in research preview.
FluxMateria is built on a fundamentally new approach to computational screening. It is not a faster version of existing simulation, and it is not another machine learning model.
FluxMateria is not a patchwork of separate models. One deterministic kernel supports life sciences, materials, and chemistry workflows from the same physics.
Speed combined with traceability, reproducibility, auditable outputs, and confidence indicators. Unlike ML/AI models, FluxMateria tells you when it's confident and when it's not.
When the cost of screening drops to near zero, the order of operations changes. ADMET profiling moves to the beginning. Materials screening becomes exhaustive. That is a structural change to R&D.
FluxMateria leads with published benchmarks, validation sets, and reproducible methodology. Every number has a methodology page. Every claim can be checked.
Right-click and save, or use the download links below. Please do not modify, recolor, or distort the logos.
Roberto Campus
Founder, FluxMateria
Try the public demos (no signup required) or request a guided walkthrough for your use case.
