ADGM · Abu Dhabi · Est. 2026

Computational Design
of Industrial Catalysts

From quantum chemistry to next-generation manufacturing. MatterForge accelerates catalyst R&D by 10–100× using a hybrid ML + quantum computing platform — and is building the first local production facility in the GCC.

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$35B+

Global catalyst market

95%

GCC import dependency

500K×

Faster than classical DFT

6–12

Months vs 5–10 years

01 — Market Context

A $35B+ market locked
in 1970s methods

The global industrial catalyst market depends entirely on ten Western companies. The GCC imports 95%+ with zero local production capacity.

98%

of all chemical production relies on industrial catalysts — developed using methods unchanged since the 1970s.

$1.5B

consumed annually in the GCC — 95%+ imported from USA, Europe, and China. Zero local suppliers.

5–10yr

development cycle per catalyst using traditional trial-and-error, costing $50–200M per project.

local production capacity in the UAE. MatterForge is building the region's first next-gen facility in KIZAD.

02 — Platform

Hybrid computing
architecture

Four integrated layers — from rapid ML screening to quantum verification — deliver chemical accuracy at industrial scale.

ML Screening — MACE & NequIP

ML potentials trained on millions of DFT calculations compute energies and activation barriers 500,000× faster than classical DFT. Thousands of configurations screened in days, not years.

Layer 01

Ab Initio Verification

CCSD(T), DMRG, and CASPT2 methods deliver chemical accuracy below 1 kcal/mol. Implemented via open-source PySCF and ORCA — free from licensing barriers.

Layer 02

Quantum Algorithms — VQE / ADAPT-VQE

Integration with IBM, Pasqal, and Quantinuum quantum hardware. QPU access via TII Abu Dhabi for active-site calculations beyond classical limits.

Layer 03

Proprietary Dataset

Built on OC20, NOMAD, and Materials Project, enriched with proprietary industrial catalytic data. Impossible to replicate with money alone.

Layer 04

03 — Our Approach

From dataset to
production facility

A four-step pipeline from problem definition to local catalyst manufacturing.

Step 01

Detailed Analysis

Comprehensive analysis of the target catalytic process. Define metrics, constraints, and success criteria.

Step 02

ML Screening × Quantum

50,000+ configurations screened via ML, then ab initio verification and quantum VQE of active sites.

Step 03

Synthesis via Partners

Top candidates synthesized through our partner network for experimental validation at client facilities.

Step 04

Transfer to Production

Scale-up via contract manufacturing or in-house KIZAD facility with anchor ADNOC offtake agreement.

04 — Pilot Project

The business case

Regional petrochemical companies spend $120–250M/year on imported catalysts. MatterForge compresses the development cycle from 5 years to 6–12 months.

Solution

CCSD(T) verification of top-50 configurations + quantum VQE of active sites
Metallocene polyolefin catalysts: predict molecular weight distribution pre-synthesis
PDH catalysts: replace Pt-Sn ($50–100/g Pt) with Ga/Zn systems via computational selection

Projected Results

$30–80M

saved per development cycle

Development cycle: 5 years → 6–12 months
Screen 50,000 configs instead of 50 in the same timeframe
Region's first proprietary catalytic dataset
Path to first local production facility in KIZAD, Abu Dhabi

05 — Case Studies

The integrated model
wins every time

Computational platforms combined with in-house production consistently outperform pure SaaS by 10× in valuation.

🔬

Drug Design · FEP+

Schrödinger

Reduced synthesized compounds 5–10× while maintaining the same clinical yield. Market cap: $3.5B.

🧬

ML Screening

Recursion Pharmaceuticals

Raised $856M including $50M from Nvidia. $150M Roche partnership + $1B Sanofi agreement.

⚗️

Generative AI

Insilico Medicine

Drug for idiopathic pulmonary fibrosis: concept to clinical trials in just 30 months.

🏭

AI Design + Manufacturing

Solugen

Combined computational design with in-house production. SaaS: $76M. Integrated: $856M.

06 — Team

Deep science.
Industrial reach.

Stepan Kharkov

Founder & CEO

Identified the GCC catalyst market gap. Strategy, fundraising, and regional partnerships.

Dmitri Volkov

Quantum Chemistry & ML

PhD. DFT, DMRG, CCSD(T), ML potentials MACE/NequIP. Core platform architecture.

Arjun Mehta

Quantum Computing

VQE/ADAPT-VQE algorithms. PennyLane/Qiskit. QPU platform integration via TII.

Khalid Al-Rashidi

Industrial Catalysis

Hydrocracking, polyolefins, PDH, ammonia synthesis. GCC Oil & Gas majors experience.

Sara Lindqvist

Commerce & Operations

ADNOC/SABIC procurement, ICV certification, and project financing for industrial facilities.

07 — Partnership Models

Three ways
to work together

$200–500K

Per Project · R&D Contract

R&D Contract

For companies testing a hypothesis

Computational screening of a specific catalytic system
Report with top-10 candidates and mechanistic rationale
Ab initio verification of best structures
Access to proprietary dataset within project scope

$1–3M

Per Year · Strategic Partnership

Strategic Partnership

For companies with serial R&D tasks

Permanent access to the platform
Priority screening in 3–5 areas simultaneously
Joint IP based on project results
Catalyst qualification at partner facilities

Custom

Consortium / Offtake

For Oil & Gas with ICV/IKTVA programs

Offtake agreement for locally produced catalysts
ICV score through KIZAD/KEZAD production
Joint venture or strategic investment
EDB financing up to 80% CAPEX

08 — Roadmap

From platform
to production plant

Q3 2026

Initialization

Foundation & Registration

Technical workshop with partners. NDAs and data agreements. Application to Hub71 Cohort 20 (August 2 deadline). ADGM registration.

Q4 2026 – Q2 2027

Platform Launch

Pilot Execution

Platform launch across four pilot areas. TII QRC partnership for QPU access. First computational reports for partners. ICV certification.

Q3 2027 – Q4 2028

Commercial Samples

Contract Manufacturing

Tolling partner manufacturing. Qualification with anchor Oil & Gas company (12–24 months). ADIO AI Innovation Grant application (up to $2.7M).

2029 – 2030

In-house Production

KIZAD Production Plant

In-house facility with EDB financing up to 80% CAPEX. Total CAPEX $50–150M. Anchor offtake contract with ADNOC as basis for project financing.

09 — How to Get Started

First results in
4–6 weeks

Technical Call

Align on catalytic task, available data, and target metrics. 60 minutes.

Technical Proposal

Detailed scope: methodology, timeline, expected results, data & IP structure, and cost.

Kickoff

NDA and data agreement. Transfer of historical catalyst data. Computational screening begins.

First Results

Baseline dataset formed. First ML-screening results delivered 4–6 weeks after project start.

Ready to accelerate
catalyst R&D?

MatterForge is the first computational catalyst platform in the GCC — combining ML, ab initio chemistry, and quantum computing to deliver results 10–100× faster.

Request a Technical Call

↗ matterforge.io ✉ contact@matterforge.io

Computational Designof Industrial Catalysts

A $35B+ market lockedin 1970s methods

Hybrid computingarchitecture