Workshops

Quantum computing has the potential to contribute to challenging problems in chemistry and materials science, but realizing that potential in the near term will require close integration of quantum computing with advances in classical computing and AI. In this talk, we will briefly review how AI can improve key components of quantum computation, including circuit optimization and quantum-state preparation. We will then present ADAPT-GQE, a generative AI framework that learns to synthesize ground-state preparation circuits for electronic-structure calculations. We demonstrate the approach on imipramine, a tricyclic antidepressant that provides a challenging, chemically relevant target for computational modelling in drug-stability protocols. ADAPT-GQE achieves order-of-magnitude reductions in circuit-generation time compared with ADAPT-VQE while maintaining comparable or improved state-preparation accuracy. These results establish a pathway toward automated quantum circuit synthesis for utility-scale quantum computational chemistry.

The process of finding new materials, optimal for a given application, is lengthy, often unpredictable, and has a low throughput. The integration of machine-learning and artificial intelligence techniques in discovery workflows can drastically accelerate the cycle, delivering a throughput otherwise impossible with first-principles methods. In this talk, I will describe two alternative computational strategies to achieve such acceleration. Firstly, I will discuss a family of machine-learning models for predicting materials quantities, with a specific example in the Curie temperature of ferromagnets. These use solely the chemical composition of a compound as feature and experimental data as target. The creation of models of this type can be drastically accelerated by the automated extraction of experimental data from published scientific literature, and by AI agentic schemes for the design of materials descriptors. Then, I will describe how an accurate description of the structure of materials, which is amenable to be used with machine learning, can offer a quantum-chemistry-accurate description of local properties at virtually no computational costs. This is based on our developed cluster expansion formulated in terms of Jacobi-Legendre polynomials. Such general framework allows us to construct, on the same footing, extremely accurate force fields as well as converged DFT charge densities. These can both be integrated in material-discovery workflows generating unprecedented speedup.

Buildings are vitally important to our economy, energy systems, and daily lives. Nationally, the commercial sector accounts for nearly 20% of primary energy use, over a third of electricity use, and nearly $200B in annual energy expenditures. 17% of our GDP is attributed to the real estate and construction industries alone. As we enter the AI era, we are asking more of our buildings than ever before – that they operate as efficient cost centers, provide low-energy, comfortable indoor environments, and increasingly, be dynamically coordinated with the power grid. This talk will highlight current trends in the industry, and research on the potential for AI to advance building performance across the lifecycle. With a focus on software tools for the operational phase, Dr. Granderson will present recent findings on how AI is being used in today's commercial products and services. She will conclude with highlights of ongoing research underway at Lawrence Berkeley National Laboratory, spanning advances in AI-ready data infrastructure, autonomous control and operation, and robotics for inspection and maintenance.

A working session from 1:15 to 4:00 PM on research priorities and opportunities for collaboration across AI and energy.

A welcome and brief introduction to the workshop, followed by the keynote on universal deep learning potentials and machine-learning-guided materials discovery.