10 Top Startups Advancing Machine Learning for Materials Science [2025]

The artificial intelligence (AI) in the chemicals and materials science sector is forecast to reach USD 28.3 billion by 2030. The market growth is driven by the increasing demand for efficient and cost-effective solutions in drug discovery and materials design. This article spotlights 10 emerging startups advancing machine learning for material science.

These innovations range from digital twin platforms and computational chemistry to nanocomposite optimization and property prediction. Companies are leveraging AI to shorten R&D cycles, enhance material performance, and create sustainable, high-performance materials.

Global Startup Heat Map highlights Emerging Machine Learning Startups for Materials Science

Through the Big Data & Artificial Intelligence (AI)-powered StartUs Insights Discovery Platform, covering over 7M+ startups, 20K+ technology trends plus 150M+ patents, news articles & market reports, we identified 110+ machine learning companies for materials science.

The Global Startup Heat Map below highlights the 10 machine learning for material science startups you should watch in 2025 as well as the geo-distribution of 110+ startups & scaleups we analyzed for this research.

According to our data, we observe high startup activity in Western Europe and the United States, followed by India. The top 5 Startup Hubs for machine learning for material science are London, New York City, New Delhi, Bangalore, and Austin.

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Explore Emerging Startups Advancing Machine Learning for Materials in 2025

We hand-picked startups to showcase in this report by filtering for their technology, founding year, location, funding, and other metrics. These ML startups for material science work on solutions ranging from material discovery and materials data management to nanocomposite optimization and computational chemistry.

1. Materium Technologies – Nanocomposite Optimization
2. Elytra Biomaterials – High-Performance Biomaterials
3. MaterialsInformation – Materials Data Management
4. N-ERGY – Material Discovery Platform
5. Atomic Tessellator – Computational Chemistry
6. Arcturian Analytics – ML-Driven Reaction Pathways
7. RoseEnergetic – Material Property Prediction
8. Radical AI – ML-assisted Material Development
9. GiwoTech – Digital Twin Platform
10. Substantial AI – Accelerated Material Discovery

1. Materium Technologies

• Founding Year: 2023
• Location: Summit, NJ, US
• Use For: Nanocomposite Optimization

Materium Technologies develops nanocomposite materials optimized through proprietary machine learning algorithms. This accelerates the discovery and refinement of complex material formulations.

The startup’s technology combines AI with materials science to predict self-assembly behaviors of multicomponent nanocomposites. It enables precise engineering of thermal, electrical, and mechanical properties without extensive physical prototyping.

Materium Technologies’ polymer nanocomposite thin films enhance the performance of products in the electronics, photonics, and packaging sectors. The proprietary coatings offer dielectric, barrier, and conformal properties.

2. Elytra Biomaterials

• Founding Year: 2023
• Location: Montreal, Canada
• Use For: High-Performance Biomaterials

Elytra Biomaterials develops an ML model that accelerates material development. This process allows the rapid iteration and refinement of biomaterials for applications like medical implants and sustainable materials for biosensors.

The startup’s ML models simulate, test, and optimize material combinations in a fraction of the time required for traditional development while maintaining precision and accuracy.

Elytra Biomaterials utilizes cues from nature to design biomaterials that are as sustainable as they are efficient. The startup combines nature-based and proprietary algorithms to engineer materials for bioelectronics, biocompatible layers for prosthetics, and micro-encapsulation materials for food and pharma sectors.

3. MaterialsInformation

• Founding Year: 2024
• Location: Northwich, UK
• Use For: Materials Data Management

MaterialsInformation offers a materials information platform that simplifies materials data management across engineering, manufacturing, and sourcing applications.

The startup structures materials data and utilizes large language models and ML to derive value from existing data. The platform’s low-code flexibility accelerates deployment and customization according to the application.

MaterialsInformation also offers a proprietary PortKey system for controlled access from code, collaborators, and customers. The platform facilitates real-time understanding of existing and new materials data. The AI-driven data processing enables faster searches and accurate insights.

4. N-ERGY

• Founding Year: 2024
• Location: Cambridge, MA, US
• Use For: Material Discovery Platform

N-ERGY develops an AI-powered material discovery platform that identifies optimal materials for extreme environments through a hybrid approach combining computational modeling with physical testing.

The startup fuses AI-driven simulations with data from numerous physically tested materials that ensures real-world performance.

N-ERGY’s solution identifies every possible material combination, narrowing down possibilities to the optimal solution, eliminating trial and error. The computational algorithm thus enables the discovery of critical material for defense, space, and electronics applications.

5. Atomic Tessellator

• Founding Year: 2024
• Location: Auckland, New Zealand
• Use For: Computational Chemistry

Atomic Tessellator provides an ab-initio, in-silico material discovery platform that speeds up the idea-simulation-result loop for small molecule chemistry and material science. The solution provides academic researchers with an intuitive user interface for materials design.

The platform optimizes design idea flow by forking simulations and testing hypothesis permutations. It also enables straightforward documentation that is accessible directly within the platform.

The startup’s GPU-accelerated visualizations give insights into simulations. The platform offloads the expensive exploratory work involved with material discovery and also enables rapid hypothesis testing.

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6. Arcturian Analytics

• Founding Year: 2024
• Location: Delaware, USA
• Use For: ML-Driven Reaction Pathways

Arcturian Analytics develops machine learning algorithms that identify optimal catalyst materials for CO2 reduction through electrolysis. The startup’s ML models predict and enhance reaction pathways for the efficient conversion of CO2 into valuable products.

The models optimize electrolysis parameters such as temperature, pressure, and voltage. This allows for the design of electrolysis reactors for CO2 capture with optimum factors like flow rates, electrode placement, and surface area.

Arcturian Analytics also provides an app that tracks carbon capture metrics in real time and automatically calculates the volume of CO2 reduced and generates corresponding carbon credits. It also uses blockchain technology to certify these generated carbon credits.

7. RoseEnergetic

• Founding Year: 2022
• Location: Munich, Germany
• Use For: Material Property Prediction

RoseEnergetic develops RoseBoom, a prediction software that transforms energetic materials research through rapid property assessment before physical synthesis occurs.

The platform combines empirical models, thermo-equilibrium codes, and machine learning algorithms to analyze molecular structures and accurately predict critical parameters.

These parameters include detonation velocity, pressure, density, and heat of formation within seconds based solely on structural formulas. RoseEnergetic thus reduces the practical work in developing new explosive materials. This increases safety and reduces the cost of development.

8. Radical AI

• Founding Year: 2024
• Location: New York, NY, US
• Use For: ML-assisted Material Development

Radical AI develops a material research and development platform that reduces the time required to create new materials. The startup uses atomistic and generative modeling to screen billions of material compositions to predict structures and physical properties. This allows for zeroing in on the materials that yield a successful experiment.

The platform also optimizes chemical synthesis through an approach that integrates computational adaptive experimentation, active learning, and self-guided scientific literature review.

Radical AI also automates synthesis and characterization through a robotic lab that executes experiments at a rate that exceeds human capacity. The process also generates valuable data to feed back into the prediction engine, closing the loop.

9. GiwoTech

• Founding Year: 2022
• Location: Boston, MA, US
• Use For: Digital Twin Platform

GiwoTech develops a digital twin platform from first principles and AI/ML simulations for biomedical, plant, and material applications. The startup’s proprietary ML models perform physics-based simulations of atomic-level molecular dynamics.

The molecular dynamics simulations provide information on conformational changes over time. The models identify compounds and design vaccines targeted at unfamiliar regions with maximum scores.

GiwoTech offers physics-based models with high predictive power and accuracy through active learning, deep learning, and high-performance computing. The atomic-level dynamic simulations of protein structural interactomes provide new, unconventional solutions for biomolecular systems.

10. Substantial AI

• Founding Year: 2021
• Location: New Delhi, India
• Use For: Accelerated Material Discovery

Substantial AI develops a material discovery platform that accelerates the creation of novel materials through advanced ML algorithms and computational modeling techniques. The startup uses data-driven techniques to predict properties of a material exhibiting non-linear behavior as a function of composition.

It reduces the number of experiments needed to discover new compositions based on target properties and compositional constraints. It allows researchers to make informed decisions for selecting raw materials based on availability without affecting the end product.

The startup optimizes a process for minimal cost, high quality, performance, and energy consumption. The platform retrieves specific data from publications and datasets using natural language processing. This assists users in designing novel gases and understanding composition-property relationships.

Discover All Emerging Startups Advancing Machine Learning for Materials Science

The emerging machine learning-based materials science startups showcased in this report are only a small sample of all startups we identified through our data-driven startup scouting approach. Download our free Industry Innovation Reports for a broad overview of the industry, or get in touch for quick & exhaustive research on the latest technologies & emerging solutions that will impact your company in 2025!