Cultivating Teachers When the School Doors Are Shut: Two Teacher-Educators Reflect on Supervision, Instruction, Change and Opportunity During the Covid-19 Pandemic

Seven weeks into our Spring 2020 semester, the Covid-19 pandemic was wreaking havoc on the world. The pandemic caused immediate shutdowns to schools and universities fundamentally changing how we plan for, teach, guide, and work with students. This paper explores how two first-year Assistant Professors navigated the challenges we faced and the learning opportunities we embraced while continuing our work as teacher educators amid a pandemic-induced shutdown. We employed collective self-study to examine our experiences while transitioning to remote learning with pre-service teachers using Moore\u27s (2012, 1993, 1989) transactional distance theory as an analytical framework to review our work as teachers in an online setting. We found that educators need to be open to continuous enhancements of instructional practices, there is a need to develop ways to equalize positions between the instructor and students, and we need to be conscious of opportunities students have to demonstrate creativity in their work. As part of this review, we developed and used a Four R\u27s Professional Inquiry Model (Recognition, Reflection, Reaction, Results) based on Moore\u27s work to help make meaning of our findings and recommendations for other practitioners

Selectivity of hydrogen chemisorption on clean and lead modified palladium particles; a TPD and photoemission study

This work describes hydrogen chemisorption on clean and lead modified palladium particles obtained from decomposition of PdO. TPD is used as a chemical probe to test the surface properties of several states of metallic palladium relevant in practical selective hydrogenation catalysts. These states differ in oxygen content and the presence of a lead modifier. XPS and UPS data serve as a basis for identifying the surface properties. TPD spectra show a very broad low temperature peak-likely bulk hydride decomposition-and a sharp TPD peak between 330 and 380 K. This latter can be devided into three rather poorly separated subpeaks; addition of Pb does not shift peak maxima but decreases the central subpeak and eliminates the high temperature peak completely. This points to the interaction of Pb with specific surface sites rather than to bulk alloy formation. The enhancement of selectivity in hydrogenation obtained from lead modification is considered as a geometric site blocking effect rather than to arise from a bulk modification of the valence electronic structure of palladium metal

Centroid Molecular Dynamics Can Be Greatly Accelerated through Neural Network Learned Centroid Forces Derived from Path Integral Molecular Dynamics

For nearly the past 30 years, centroid molecular dynamics (CMD) has proven to be a viable classical-like phase space formulation for the calculation of quantum dynamical properties. However, calculation of the centroid effective force remains a significant computational cost and limits the ability of CMD to be an efficient approach to study condensed phase quantum dynamics. In this paper, we introduce a neural network-based methodology for first learning the centroid effective force from path integral molecular dynamics data, which is subsequently used as an effective force field to evolve the centroids directly with the CMD algorithm. This method, called machine-learned centroid molecular dynamics (ML-CMD), is faster and far less costly than both standard "on the fly" CMD and ring polymer molecular dynamics (RPMD). The training aspect of ML-CMD is also straightforwardly implemented utilizing the DeepMD software kit. ML-CMD is then applied to two model systems to illustrate the approach: liquid para-hydrogen and water. The results show comparable accuracy to both CMD and RPMD in the estimation of quantum dynamical properties, including the self-diffusion constant and velocity time correlation function, but with significantly reduced overall computational cost

Coarse-Graining with Equivariant Neural Networks: A Path Towards Accurate and Data-Efficient Models

Machine learning has recently entered into the mainstream of coarse-grained (CG) molecular modeling and simulation. While a variety of methods for incorporating deep learning into these models exist, many of them involve training neural networks to act directly as the CG force field. This has several benefits, the most significant of which is accuracy. Neural networks can inherently incorporate multi-body effects during the calculation of CG forces, and a well-trained neural network force field outperforms pairwise basis sets generated from essentially any methodology. However, this comes at a significant cost. First, these models are typically slower than pairwise force fields even when accounting for specialized hardware which accelerates the training and integration of such networks. The second, and the focus of this paper, is the need for the considerable amount of data needed to train such force fields. It is common to use tens of microseconds of molecular dynamics data to train a single CG model, which approaches the point of eliminating the CG models usefulness in the first place. As we investigate in this work, it is apparent that this data-hunger trap from neural networks for predicting molecular energies and forces is caused in large part by the difficulty in learning force equivariance, i.e., the fact that force vectors should rotate while maintaining their magnitude in response to an equivalent rotation of the system. We demonstrate that for CG water, networks that inherently incorporate this equivariance into their embedding can produce functional models using datasets as small as a single frame of reference data, which networks without inherent symmetry equivariance cannot

Utilizing Machine Learning to Greatly Expand the Range and Accuracy of Bottom-Up Coarse-Grained Models through Virtual Particles

Coarse-grained (CG) models parametrized using atomistic reference data, i.e., "bottom up" CG models, have proven useful in the study of biomolecules and other soft matter. However, the construction of highly accurate, low resolution CG models of biomolecules remains challenging. We demonstrate in this work how virtual particles, CG sites with no atomistic correspondence, can be incorporated into CG models within the context of relative entropy minimization (REM) as latent variables. The methodology presented, variational derivative relative entropy minimization (VD-REM), enables optimization of virtual particle interactions through a gradient descent algorithm aided by machine learning. We apply this methodology to the challenging case of a solvent-free CG model of a 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) lipid bilayer and demonstrate that introduction of virtual particles captures solvent-mediated behavior and higher-order correlations which REM alone cannot capture in a more standard CG model based only on the mapping of collections of atoms to the CG sites

Utilizing Machine Learning to Greatly Expand the Range and Accuracy of Bottom-Up Coarse-Grained Models Through Virtual Particles

Coarse-grained (CG) models parameterized using atomistic reference data, i.e., 'bottom up' CG models, have proven useful in the study of biomolecules and other soft matter. However, the construction of highly accurate, low resolution CG models of biomolecules remains challenging. We demonstrate in this work how virtual particles, CG sites with no atomistic correspondence, can be incorporated into CG models within the context of relative entropy minimization (REM) as latent variables. The methodology presented, variational derivative relative entropy minimization (VD-REM), enables optimization of virtual particle interactions through a gradient descent algorithm aided by machine learning. We apply this methodology to the challenging case of a solvent-free CG model of a 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) lipid bilayer and demonstrate that introduction of virtual particles captures solvent-mediated behavior and higher-order correlations which REM alone cannot capture in a more standard CG model based only on the mapping of collections of atoms to the CG sites.Comment: 35 pages, 9 figure

The starburst phenomenon from the optical/near-IR perspective

The optical/near-IR stellar continuum carries unique information about the stellar population in a galaxy, its mass function and star-formation history. Star-forming regions display rich emission-line spectra from which we can derive the dust and gas distribution, map velocity fields, metallicities and young massive stars and locate shocks and stellar winds. All this information is very useful in the dissection of the starburst phenomenon. We discuss a few of the advantages and limitations of observations in the optical/near-IR region and focus on some results. Special attention is given to the role of interactions and mergers and observations of the relatively dust-free starburst dwarfs. In the future we expect new and refined diagnostic tools to provide us with more detailed information about the IMF, strength and duration of the burst and its triggering mechanisms.Comment: 6 pages, 3 figures, to appear in "Starbursts: from 30 Doradus to Lyman Break Galaxies" 2005, eds. R. de Grijs and R. M. Gonzalez Delgado (Kluwer

Reducing the Read Noise of the James Webb Space Telescope Near Infrared Spectrograph Detector Subsystem

We describe a Wiener optimal approach to using the reference output and reference pixels that are built into Teledyne's HAWAII-2RG detector arrays. In this way, we are reducing the total noise per approximately 1000 second 88 frame up-the-ramp dark integration from about 6.5 e- rms to roughly 5 e- rms. Using a principal components analysis formalism, we achieved these noise improvements without altering the hardware in any way. In addition to being lower, the noise is also cleaner with much less visible correlation. For example, the faint horizontal banding that is often seen in HAWAII-2RG images is almost completely removed. Preliminary testing suggests that the relative gains are even higher when using non flight grade components. We believe that these techniques are applicable to most HAWAII-2RG based instruments