What We Do
Smart Resources. Advanced Computing. Real-World Impact.
SRCTECH combines fundamental science, advanced computing, and applied artificial intelligence to transform
complex research problems into deployable technologies.
Digital R&D Platforms
ULAM, SENTOR, and other research platforms developed by SRCTECH.
Scientific Software & HPC
Scientific software development, high-performance computing, and workflow automation.
Engineering & Industrial Solutions
Industrial simulation, optimization, composite materials, and advanced engineering solutions.
Artificial Intelligence & Data
Artificial intelligence, machine learning, data analytics, and decision-support systems.
Energy Technologies
Hydrogen, energy storage, photovoltaic systems, and sustainable energy materials.
Research & Innovation
R&D projects, scientific publications, academic partnerships, and technology development.
Computational Materials Science
Atomistic modeling, DFT calculations, materials simulation, and property prediction.
Training, Consulting & Certification
Technical training, corporate consulting, applied programs, and verifiable certificates.
The planned modules will analyze features extracted from crystal structures, evaluate potential stability trends before expensive calculations are performed, and prioritize large collections of candidate materials. This approach will help direct limited computational resources toward the most promising candidates.
Machine-learning outputs will not be presented as definitive experimental results. Instead, predictions will be reported with confidence scores and supporting information to assist researchers in scientific decision-making.
Through this module, researchers will be able to configure computational resources, submit jobs to SLURM and similar scheduling systems, monitor active calculations, and examine results through a centralized interface. Parameters such as CPU count, memory allocation, wall time, and queue selection will be validated according to system capabilities.
Researchers will be able to select crystal structures from the repository and transfer them directly into the ULAM computational workflow. If a required structure is unavailable, users will be able to generate a new crystal structure using lattice parameters, space-group information, atomic species, and atomic coordinates.
