Session C08: Cold molecular collisions

Live

In the past decades, many advances have been made in producing and controlling molecules at ultracold temperatures. An important research frontier for ultracold molecules is understanding how they collide. Compared to collisions between atoms, molecular collisions are much richer and can exhibit qualitatively new phenomena such as ``sticky collisions.'' Experimentally, much work has been done to explore inelastic collisions for both reactive and non-reactive bi-alkali molecules in bulk samples. Recent advances in laser-cooling $^2\Sigma$ molecules have made them available for experimental studies. In contrast to bi-alkalis, $^2\Sigma$ molecules have unpaired electron spins, leading to new features such as a large hyperfine splitting, spin-rotational structure, and intermolecular electronic spin-spin coupling. In this talk, I will present our recent work on measuring collisional loss between two laser-cooled $^2\Sigma$ CaF molecules in their absolute internal ground state. By merging two optical tweezers, each containing a single CaF molecule, we control the exact number of molecules participating in the collision. This tweezer-based approach allows exquisite control over the internal state, and together with laser-cooling, allows one to approach temperatures near the single partial wave regime. I will report on the collisional loss rates measured in a variety of different hyperfine states and discuss the results.   

Live

Advances in AMO techniques enabled the creation of ultracold samples of molecular species and opened opportunities to explore chemistry in the ultralow temperature regime. We report progress toward a detailed microscopic picture of molecules transforming from one species to another. So far, most studies of ultracold collisions rely on a loss-of-molecules signal. To extend such studies into the short-range where chemistry takes place, we combine the production of quantum-state-selected ultracold KRb molecules with ion mass and kinetic energy spectrometry, and directly observed KRb $+$ KRb reaction intermediates and products. Such direct detections allow further studies, including measuring the transient reaction intermediate lifetime, steering the reaction pathway with light, and investigating the product state distribution.   

Live

Laser cooling of atoms has enabled the production of diatomic molecules at a phase-space density that is sufficiently large for quantum degeneracy effects to be important. Extremely low temperatures have also allowed the confinement of these molecules in electric, magnetic, and optical traps, where they are isolated from their environment and can be carefully studied. A significant effort from the scientific community is now devoted to study the interactions and collisional dynamics between such molecules or with atoms. Here, I will describe our quantum simulations of collisions and reactivity of three- and four- atom systems by explicitly addressing individual ro-vibrational states. The choice of molecules is inspired by ongoing and planned US-based experiments. Quantum defect theory was also applied and leads to useful intuitive insights. Finally, we revisit time-dependent classical simulations to investigate collisions between non-reactive molecules in order to isolate the processes that lead to the severe limitations on the experimentally-observed lifetimes in optical dipole potentials.   

On Demand

I will describe how quantum dynamics calculations can benefit from Bayesian machine learning (ML). In particular, I will show that a hybrid approach, where ML is used to model the Hamiltonian parameters and parse the solutions of the Schrodinger equation, yields accurate quantum predictions with fewer calculations than traditional approaches; produces not only quantum observables but also the uncertainties of these observables stemming from limitations imposed by the difficulty of solving the Schrodinger equation; can be used to solve the inverse scattering problem; and can make accurate predictions of quantum observables for systems and experimental conditions that are currently out of reach of rigorous quantum theory. I will illustrate that it is possible to build Bayesian models that can extrapolate quantum solutions in the Hamiltonian parameter space. This can be used potentially to discover new physics of quantum systems at the Hamiltonian parameters, where neither theory nor experiment are feasible. Finally, I will illustrate that ML can be used to correct the results of approximate quantum dynamics calculations, offering a system- and approximation-independent approach to enhance the accuracy of quantum predictions. I will discuss how these methods can advance quantum theory of ultracold molecules. References: PCCP 21, 13392 (2019); PRL 121, 255702 (2018); PRL 115, 073202 (2015).