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Green Hydrogen

Innovate,Manufacture, Deploy & Operate

H2 Lifecycle Management - Innovators, Developers, Financial Institutions

H2 Lifecycle Management | Innovators

Predicting material properties is a laborious and expensive process, especially when it comes to synthesizing and validating novel materials in the physical world. However, there is a promising solution offered by Large Language Models [LLM] & other Neural Networking [NN] techniques, which significantly reduce the time and cost involved in materials development.

Through the utilization of LLM & other NN algorithms, inputs such as crystal structure, chemical composition, and electronic band structures can be used to train models that predict computationally challenging or costly properties. Moreover, ML empowers scientists to generate innovative ideas for intricate tasks like solid-state synthesis and crystal structure determination, unveiling previously inaccessible physical insights. This efficient approach allows scientists to explore extensive chemical and configurational search spaces in pursuit of stable, affordable, and high-performance materials.

Our engine, powered by LLM and High Performance Computing [HPC], facilitates atomistic as well as molecular generative materials design. With the support of Azure computing resources, the engine seamlessly integrates various stages of the materials design process, including hypothesis generation, calculation setup, experiment execution, informatics analysis, and database development. The need for human intervention is minimized, enabling a more efficient and streamlined workflow.

LDES Lifecycle Management | Innovators | Applications

Computational Tools

Atoms Engine

Experience the transformative power of our LLM powered atomistic computational model. Enhancing precision and efficiency, Atoms Engine offers guidance, troubleshooting, and expert insights. Optimize your research outcomes with advanced parameter tuning, methodology discussions, and data analysis. Stay at the forefront through literature reviews and collaborate on experimental design. Unleash new frontiers in materials science and atomistic simulations.

Molecular Engine

Our trained neural network generates and predicts molecular electron densities. The engine utilizes generative AI, HPC, and a proprietary parallel simulation architecture, integrating transformers, graph neural networks, and generative adversarial networks. With minimal human intervention, it facilitates hypothesis generation, calculations, experiments, informatics analysis, and database development, advancing computational chemistry.

Materials Engine

Our LLM-powered engine facilitates atomistic generative materials design. Supported by Azure Open AI and HPC resources, it seamlessly integrates hypothesis generation, calculation setup, experiment execution, informatics analysis, and database development with minimal human intervention. At its core, the engine features a proprietary neural network that leverages the synergistic potential of Quantum Monte Carlo (QMC) and Coupled Cluster (CC) methods, ensuring advanced computational accuracy and predictive power for materials design.

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Materials Database & Discovery

Materials AI

Utilize LLM's to facilitate the retrieval of materials information based on chemical attributes, composition, or inherent properties. Employ the obtained data to formulate testable hypotheses and execute experiments, while subjecting the findings to rigorous adversarial peer review empowered by Azure Open AI.

Molecules AI

Engage in the systematic exploration of data pertaining to molecules with relevance to battery electrolytes. Utilize the acquired data to formulate informed hypotheses and subsequently initiate experimental investigations. Employ the power of GPT-4 to facilitate rigorous adversarial peer review, enabling critical evaluation and constructive feedback on the experimental findings.

LDES Materials AI

Conduct an exhaustive exploration of potential candidate materials for LDES technologies, focusing on their predicted voltage profiles and oxygen evolution data. Employ the outcomes of this exploration to formulate well-founded hypotheses and initiate experimental investigations. Leverage the advanced capabilities of GPT-4 to empower adversarial peer review.

Synthesis AI

Systematically search for synthesis recipes extracted from reputable literature sources utilizing LLMs. Employ the acquired results to generate well-formulated hypotheses, which can then be tested through meticulously designed experiments. Leverage the advanced capabilities of GPT-4 to facilitate adversarial peer review, empowering critical evaluation and constructive feedback on the experimental outcomes.

Catalysis AI

Systematically retrieve catalysis data from our specialized Datasets using Language Models for Low-Level Materials LLMs. Utilize the obtained results to generate scientifically grounded hypotheses, which can subsequently be tested through meticulously designed experimental investigations. Harness the power of GPT-4 to enable adversarial peer review, facilitating critical evaluation and constructive feedback on the experimental outcomes.

MOF

Systematically explore and retrieve comprehensive data on metal-organic frameworks [MOFs] and coordination polymers from the specialized QMOF Database, employing LLMs. Utilize the acquired dataset to formulate informed hypotheses, which can be tested through meticulously designed experimental investigations. Harness the advanced capabilities of GPT-4 to facilitate adversarial peer review, empowering critical evaluation and constructive feedback on the experimental outcomes.

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Materials Data Analysis Tools

PhasePal

Our Phase AI helps with DFT calculations to generate compositional phase diagrams. It further enhances the process by providing valuable insights, aiding data analysis, and supporting decision-making. It interprets and analyzes data, generates hypotheses, suggests exploration strategies, and offers decision support. By leveraging ChatGPT's capabilities, researchers can gain a deeper understanding of phase behaviors, optimize exploration, and make informed decisions.

PourbaixGen

Leverage the power of natural language to generate stability diagrams for solids in aqueous solutions and experimental data on ions using our HPC-based QMC + CC Neural Network. Analyze and interpret data, generate hypotheses, provide suggestions for exploration strategies, and offer decision support. By harnessing the capabilities of ChatGPT, gain profound insights into phase behaviors, optimize exploration efforts, and make well-informed decisions.

Achilles

Leverage the capabilities of Generative AI to generate novel crystal structures through transformations encompassing species substitution or removal, temperature or pressure variations, and the introduction of structural defects or superstructures. Enable the autogeneration of hypotheses and seamless virtual experimentation to identify optimal crystal structures, all while effortlessly updating your materials database at the click of a button.

ReSolids

Unleash untapped potential in the realm of solid-state reactions by harnessing the vast insights provided by a comprehensive database of Quantum Monte Carlo (QMC) + Coupled Cluster (CC) computational results.

SolidSynth

Conduct extensive-scale computations to evaluate the mixing ratios of two solid substances in contact, enabling the precise determination of reaction energy. Facilitate the automatic generation of hypotheses and streamline virtual experimentation to ascertain optimal compatibility. Seamlessly update your materials reaction database using natural language, ensuring a comprehensive and up-to-date repository of information.

RoboSpectra

Employ exa-scale computation and visualization to analyze X-ray absorption spectra for multiple materials concurrently, utilizing the capabilities of natural language processing. Harness the power of generative AI to enhance the precision of structural parameters and improve the estimation of uncertainties. Streamline the process by automating sample preparation and experiment execution, optimizing efficiency and productivity in materials analysis.

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Green Hydrogen

Innovate,Manufacture, Deploy & Operate

H2 Lifecycle Management - Innovators, Developers, Financial Institutions

H2 Lifecycle Management | Innovators

LDES Lifecycle Management | Innovators | Applications

Computational Tools

Materials Database & Discovery

Materials Data Analysis Tools

Partners