Abstract: Various aspects of the present disclosure relate to techniques for recursive generative planning system with verification modules and user feedback integration. An apparatus is configured to execute a recursive planning pipeline that integrates at least one generative model and at least one rule-based model to generate candidate transformation pathways for a target structure, apply one or more verification modules to evaluate validity, stability, and coherence of intermediate outputs, selectively regenerate portions of the candidate transformation pathways from a user-specified stage while preserving validated portions, and incorporate user feedback to refine subsequent pathway generation and model operation.

Abstract: Various aspects of the present disclosure relate to techniques for generative-discriminative artificial-intelligence framework for digital-twin discovery of thin-film materials.

Abstract: Various aspects of the present disclosure relate to techniques for computational target identification and validation. An apparatus is configured to determine protein structure data associated with a plurality of genes, wherein the protein structure data describes one or more protein structures, simulate interactions between a plurality of compounds and the one or more protein structures, determine binding affinities between each of the plurality of compounds and the one or more protein structures, and generate a list of validated or novel targets based on the binding affinities.

Abstract: Apparatuses, methods, program products, and systems are disclosed for AI-based drug side effect prediction. An apparatus is configured to determine molecular structure information for one or more molecules, predict one or more potential side effects based on the molecular structure information using a machine learning model, and provide the predicted one or more potential side effects to a user.

Abstract: Various aspects of the present disclosure relate to techniques for a cloud scientific machine learning programming environment. An apparatus includes at least one memory and at least one processor coupled to the memory and configured to cause the apparatus to receive a request to perform a machine learning task, analyze the machine learning task to determine one or more functions for performing the machine learning task, generate a workflow for the one or more functions of the machine learning task, execute the generated workflow, and provide results of the executed workflow.

Abstract: Apparatuses, systems, computer program products, and methods are disclosed for foundation model based fluid simulations. An apparatus includes a processor and a memory that stores code executable by the processor to receive a fluid foundation model that is pretrained on fluid data, deploy the received fluid foundation model into a downstream machine learning pipeline for a fluid dynamics application, reconfigure the fluid foundation model for the fluid dynamics application, and output results from the machine learning pipeline for the fluid dynamics application based on the reconfigured fluid foundation model.

Abstract: Apparatuses, systems, computer program products, and methods are disclosed for differentiable machines for physical systems. A hardware server device is configured to determine a plurality of differentiable models each representing a component of a physical system. A hardware server device is configured to combine a plurality of differentiable models using an integration layer so that the integration layer and the combined differentiable models form a differentiable machine representing a physical system. A hardware server device is configured to deploy a differentiable machine for an instance of a physical system.