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Abstract We measured the rotationally resolved infrared spectra of helium solvated methyl fluoride at 3 μm and 10 μm, wherein lies C−H and C−F stretching bands, respectively. The linewidths (FWHM) were found to increase with increasing vibrational energy and range from 0.002 cm⁻¹in thev₃band (C−F stretch) at ~1047 cm⁻¹, to 0.65 cm⁻¹in thev₄band (asymmetric C−H stretch) at ~2997 cm⁻¹. In between these two bands we observed the lower and upper components of the Fermi triad bands (ν₁/2ν₂/2ν₅) at ~2859 and ~2961 cm⁻¹. We carried out Stark spectroscopy on the lower band on account of its narrower linewidths (0.04 vs. 0.14 cm⁻¹, respectively). The objective of performing Stark spectroscopy was to see if there is any evidence for a rotational linewidth dependence on the external field strength, due to a reduced difference in between methyl fluorides rotational energy gap and the roton‐gap of superfluid helium. We did not find any evidence for such an effect, which we largely attribute to the rotational energy gap not increasing significantly enough by the external field. We point to another molecule (formaldehyde) whose energy levels are predicted to show a more promising response to application of an external field. 

The formic acid-ammonia dimer is an important example of a hydrogen-bonded complex in which a double proton transfer can occur. Its microwave spectrum has recently been reported and rotational constants and quadrupole coupling constants were determined. Calculated estimates of the double-well barrier and the internal barriers to rotation were also reported. Here we report a full-dimensional potential energy surface (PES) for this complex, using two closely related Δ-machine learning methods to bring it to the CCSD(T) level of accuracy. The PES dissociates smoothly and accurately. Using a 2d quantum model the ground vibrational-state tunneling splitting is estimted to be less than 10−4 cm−1. The dipole moment along the intrinsic reaction coordinate is calculated along with a Mullikan charge analysis and supports mildly ionic character of the minimum and strongly ionic character at the double-well barrier. 

Abstract The discovery of a new kind of experience can teach an agent what that kind of experience is like. Such a discovery can be epistemically transformative, teaching an agent something they could not have learned without having that kind of experience. However, learning something new does not always require new experience. In some cases, an agent can merely expand their existing knowledge using, e.g., inference or imagination that draws on prior knowledge. We present a computational framework, grounded in the language of partially observable Markov Decision Processes (POMDPs), to formalize this distinction. We propose that epistemically transformative experiences leave a measurable “signature” distinguishing them from experiences that are not epistemically transformative. For epistemically transformative experiences, learning in a new environment may be comparable to “learning from scratch” (since prior knowledge has become obsolete). In contrast, for experiences that are not transformative, learning in a new environment can be facilitated by prior knowledge of that same kind (since new knowledge can be built upon the old). We demonstrate this in a synthetic experiment inspired by Edwin Abbott’s Flatland, where an agent learns to navigate a 2D world and is subsequently transferred either to a 3D world (epistemically transformative change) or to an expanded 2D world (epistemically non-transformative change). Beyond the contribution to understanding epistemic change, our work shows how tools in computational cognitive science can formalize and evaluate philosophical intuitions in new ways. 

We propose a design paradigm for multistate machines where transitions from one state to another are organized by bifurcations of multiple equilibria of the energy landscape describing the collective interactions of the machine components. This design paradigm is attractive since, near bifurcations, small variations in a few control parameters can result in large changes to the system’s state providing an emergent lever mechanism. Further, the topological configuration of transitions between states near such bifurcations ensures robust operation, making the machine less sensitive to fabrication errors and noise. To design such machines, we develop and implement a new efficient algorithm that searches for interactions between the machine components that give rise to energy landscapes with these bifurcation structures. We demonstrate a proof of concept for this approach by designing magnetoelastic machines whose motions are primarily guided by their magnetic energy landscapes and show that by operating near bifurcations we can achieve multiple transition pathways between states. This proof of concept demonstration illustrates the power of this approach, which could be especially useful for soft robotics and at the microscale where typical macroscale designs are difficult to implement. 

We analyzed a population-based cohort ( N = 10,922) to investigate the onset and stability of racial and ethnic disparities in advanced (i.e., above the 90 th percentile) science and mathematics achievement during elementary school as well as the antecedent, opportunity, and propensity factors that explained these disparities. About 13% to 16% of White students versus 3% to 4% of Black or Hispanic students displayed advanced science or mathematics achievement during kindergarten. The antecedent factor of family socioeconomic status and the propensity factors of student science, mathematics, and reading achievement by kindergarten consistently explained whether students displayed advanced science or mathematics achievement during first, second, third, fourth, or fifth grade. These and additional factors substantially or fully explained initially observed disparities between Black or Hispanic and White students in advanced science or mathematics achievement during elementary school. Economic and educational policies designed to increase racial and ethnic representation in STEM course taking, degree completion, and workforce participation may need to begin by elementary school.