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57Fe nuclear resonance vibrational spectroscopy (NRVS) is used to study the tetranuclear iron clusters bearing a terminal Fe(iii)–O/OH moiety. The redox states of the three remote basal iron sites modulate the Fe(iii)–O/OH vibrational frequencies. 

Abstract Extremophile organisms are known that can metabolize at temperatures down to − 25 °C (psychrophiles) and up to 122 °C (hyperthermophiles). Understanding viability under extreme conditions is relevant for human health, biotechnological applications, and our search for life elsewhere in the universe. Information about the stability and dynamics of proteins under environmental extremes is an important factor in this regard. Here we compare the dynamics of small Fe-S proteins – rubredoxins – from psychrophilic and hyperthermophilic microorganisms, using three different nuclear techniques as well as molecular dynamics calculations to quantify motion at the Fe site. The theory of ‘corresponding states’ posits that homologous proteins from different extremophiles have comparable flexibilities at the optimum growth temperatures of their respective organisms. Although ‘corresponding states’ would predict greater flexibility for rubredoxins that operate at low temperatures, we find that from 4 to 300 K, the dynamics of the Fe sites in these homologous proteins are essentially equivalent. 

The impact of randomness on model training is poorly understood. How do differences in data order and initialization actually manifest in the model, such that some training runs outperform others or converge faster? Furthermore, how can we interpret the resulting training dynamics and the phase transitions that characterize different trajectories? To understand the effect of randomness on the dynamics and outcomes of neural network training, we train models multiple times with different random seeds and compute a variety of metrics throughout training, such as the norm, mean, and variance of the neural network's weights. We then fit a hidden Markov model (HMM) over the resulting sequences of metrics. The HMM represents training as a stochastic process of transitions between latent states, providing an intuitive overview of significant changes during training. Using our method, we produce a low-dimensional, discrete representation of training dynamics on grokking tasks, image classification, and masked language modeling. We use the HMM representation to study phase transitions and identify latent "detour" states that slow down convergence. 

Abstract Solid–liquid phase transitions are basic physical processes, but atomically resolved microscopy has yet to capture their full dynamics. A new technique is developed for controlling the melting and freezing of self‐assembled molecular structures on a graphene field‐effect transistor (FET) that allows phase‐transition behavior to be imaged using atomically resolved scanning tunneling microscopy. This is achieved by applying electric fields to 2,3,5,6‐tetrafluoro‐7,7,8,8‐tetracyanoquinodimethane‐decorated FETs to induce reversible transitions between molecular solid and liquid phases at the FET surface. Nonequilibrium melting dynamics are visualized by rapidly heating the graphene substrate with an electrical current and imaging the resulting evolution toward new 2D equilibrium states. An analytical model is developed that explains observed mixed‐state phases based on spectroscopic measurement of solid and liquid molecular energy levels. The observed nonequilibrium melting dynamics are consistent with Monte Carlo simulations. 

Abstract Isotopic fractionation has been linked to the lattice vibrations of materials through their phonon spectra. The Lamb-Mössbauer factor (fLM) has the potential to provide information about the lattice vibrations in materials. We constrain the temperature evolution of the fLM of γ- and ε-Fe at in situ high-P-T conditions between 1650 K and the melting point. We find that the vibrations of γ- and ε-Fe can be described using a quasiharmonic model with a pressure- and temperature-dependent Debye temperature computed from the measured fLM. From the Debye temperature, we derive the equilibrium isotopic fractionation β-factor of iron. Our results show that the quasiharmonic behavior of metallic iron would lower the value of lnβFe57/54 by 0.1‰ at 1600–2800 K and 50 GPa when compared to the extrapolation of room temperature nuclear resonant inelastic X-ray scattering data. Our study suggests that anharmonicity may be more prevalent in Fe metal than in lower mantle minerals at 2800 K and 50 GPa, a relevant condition for the core formation, and the silicate mantle may be isotopically heavy in iron. 

Important natural resources in the Arctic rely heavily on sea ice, making it important to forecast Arctic sea ice changes. Arctic sea ice forecasting often involves two connected tasks: sea ice concentration at each pixel and overall sea ice extent. Instead of having two separate models for two forecasting tasks, in this report, we study how to use multi-task learning techniques and leverage the connections between ice concentration and ice extent to improve accuracy for both prediction tasks. Because of the spatiotemporal nature of the data, we designed two novel multi-task learning models based on CNNs and ConvLSTMs, respectively. We also developed a custom loss function which trains the models to ignore land pixels when making predictions. Our experiments show our models can have better accuracies than separate models that predict sea ice extent and concentration separately, and that our accuracies are better than or comparable with results in the state-of-the-art studies.