Patrickdunlap5248

The recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a "best" catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem "What is the set of catalysts that is worth testing experimentally?" Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as "worth investigating" or "not worth investigating" experimentally. Our results show an ∼7-16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets a larger, synthesized dataset and a smaller, real dataset.The benzene-ethene and parallel-displaced (PD) benzene-benzene dimers are the most fundamental systems involving π-π stacking interactions. Several high-level ab initio investigations calculated the binding energies of these dimers using the coupled-cluster with singles, doubles, and quasi-perturbative triple excitations [CCSD(T)] method at the complete basis set [CBS] limit using various approaches such as reduced virtual orbital spaces and/or MP2-based basis set corrections. Here, we obtain CCSDT(Q) binding energies using a Weizmann-3-type approach. In particular, we extrapolate the self-consistent field (SCF), CCSD, and (T) components using large heavy-atom augmented Gaussian basis sets [namely, SCF/jul-cc-pV5,6Z, CCSD/jul-cc-pVQ,5Z, and (T)/jul-cc-pVT,QZ]. We consider post-CCSD(T) contributions up to CCSDT(Q), inner-shell, scalar-relativistic, and Born-Oppenheimer corrections. Overall, our best relativistic, all-electron CCSDT(Q) binding energies are ∆Ee,all,rel = 1.234 (benzene-ethene) and 2.550 (b binding energies.Neural networks (NNs) are employed to predict equations of state from a given isotropic pair potential using the virial expansion of the pressure. The NNs are trained with data from molecular dynamics simulations of monoatomic gases and liquids, sampled in the NVT ensemble at various densities. We find that the NNs provide much more accurate results compared to the analytic low-density limit estimate of the second virial coefficient and the Carnahan-Starling equation of state for hard sphere liquids. Furthermore, we design and train NNs for computing (effective) pair potentials from radial pair distribution functions, g(r), a task that is often performed for inverse design and coarse-graining. Providing the NNs with additional information on the forces greatly improves the accuracy of the predictions since more correlations are taken into account; the predicted potentials become smoother, are significantly closer to the target potentials, and are more transferable as a result.Inclusion of hydrodynamic interactions is essential for a quantitatively accurate Brownian dynamics simulation of colloidal suspensions or polymer solutions. We use the generalized Rotne-Prager-Yamakawa (GRPY) approximation, which takes into account all long-ranged terms in the hydrodynamic interactions, to derive the complete set of hydrodynamic matrices in different geometries unbounded space, periodic boundary conditions of Lees-Edwards type, and vicinity of a free surface. The construction is carried out both for non-overlapping as well as for overlapping particles. We include the dipolar degrees of freedom, which allows one to use this formalism to simulate the dynamics of suspensions in a shear flow and to study the evolution of their rheological properties. Finally, we provide an open-source numerical package, which implements the GRPY algorithm in Lees-Edwards periodic boundary conditions.The deposition of pathological protein aggregates in the brain plays a central role in cognitive decline and structural damage associated with neurodegenerative diseases. In Alzheimer's disease, the formation of amyloid-β plaques and neurofibrillary tangles of the tau protein is associated with the appearance of symptoms and pathology. Detailed models for the specific mechanisms of aggregate formation, such as nucleation and elongation, exist for aggregation in vitro where the total protein mass is conserved. However, in vivo, an additional class of mechanisms that clear pathological species is present and is believed to play an essential role in limiting the formation of aggregates and preventing or delaying the emergence of disease. A key unanswered question in the field of neuro-degeneration is how these clearance mechanisms can be modeled and how alterations in the processes of clearance or aggregation affect the stability of the system toward aggregation. selleck compound Here, we generalize classical models of protein aggregation to take into account both production of monomers and the clearance of protein aggregates. We show that, depending on the specifics of the clearance process, a critical clearance value emerges above which accumulation of aggregates does not take place. Our results show that a sudden switch from a healthy to a disease state can be caused by small variations in the efficiency of the clearance process and provide a mathematical framework to explore the detailed effects of different mechanisms of clearance on the accumulation of aggregates.