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The electronic Schrödinger equation can only be solved analytically for the hydrogen atom, and the numerically exact full configuration-interaction method is exponentially expensive in the number of electrons. Quantum Monte Carlo methods are a possible way out: they scale well for large molecules, they can be parallelized and their accuracy has, as yet, been only limited by the flexibility of the wavefunction ansatz used. Here we propose PauliNet, a deep-learning wavefunction ansatz that achieves nearly exact solutions of the electronic Schrödinger equation for molecules with up to 30 electrons. PauliNet has a multireference Hartree–Fock solution built in as a baseline, incorporates the physics of valid wavefunctions and is trained using variational quantum Monte Carlo. PauliNet outperforms previous state-of-the-art variational ansatzes for atoms, diatomic molecules and a strongly correlated linear H10, and matches the accuracy of highly specialized quantum chemistry methods on the transition-state energy of cyclobutadiene, while being computationally efficient.High-accuracy quantum chemistry methods struggle with a combinatorial explosion of Slater determinants in larger molecular systems, but now a method has been developed that learns electronic wavefunctions with deep neural networks and reaches high accuracy with only a few determinants. The method is applicable to realistic chemical processes such as the automerization of cyclobutadiene.

Non-covalent π − π interactions are central to chemical and biological processes, yet the full understanding of their origin that would unite the simplicity of empirical approaches with the accuracy of quantum calculations is still missing. Here we employ a quantum-mechanical Hamiltonian model for van der Waals interactions, to demonstrate that intermolecular electron correlation in large supramolecular complexes at equilibrium distances is appropriately described by collective charge fluctuations. We visualize these fluctuations and provide connections both to orbital-based approaches to electron correlation, as well as to the simple London pairwise picture. The reported binding energies of ten supramolecular complexes obtained from the quantum-mechanical fluctuation model joined with density functional calculations are within 5% of the reference energies calculated with the diffusion quantum Monte-Carlo method. Our analysis suggests that π − π stacking in supramolecular complexes can be characterized by strong contributions to the binding energy from delocalized, collective charge fluctuations—in contrast to complexes with other types of bonding. Attractive, non-covalent interactions between aromatic rings—termed π − π stacking—is common in chemistry but difficult to model. Here the authors report a quantum-mechanical model to show the importance of collective charge fluctuations for understanding pi-stacked supramolecular systems.

Mutual Coulomb interactions between electrons lead to a plethora of interesting physical and chemical effects, especially if those interactions involve many fluctuating electrons over large spatial scales. Here, we identify and study in detail the Coulomb interaction between dipolar quantum fluctuations in the context of van der Waals complexes and materials. Up to now, the interaction arising from the modification of the electron density due to quantum van der Waals interactions was considered to be vanishingly small. We demonstrate that in supramolecular systems and for molecules embedded in nanostructures, such contributions can amount to up to 6 kJ/mol and can even lead to qualitative changes in the long-range van der Waals interaction. Taking into account these broad implications, we advocate for the systematic assessment of so-called Dipole-Correlated Coulomb Singles in large molecular systems and discuss their relevance for explaining several recent puzzling experimental observations of collective behavior in nanostructured materials. High-level methods to describe van der Waals interactions are limited due to their computational cost. This work introduces a new theoretical approach, that extends the dipolar many-body dispersion formalism to higher-order contributions, demonstrated to be applicable to practically-relevant systems and nano-environments.

To demonstrate the feasibility of robotic middle ear access in a clinical setting, nine adult patients with severe-to-profound hearing loss indicated for cochlear implantation were included in this clinical trial. A keyhole access tunnel to the tympanic cavity and targeting the round window was planned based on preoperatively acquired computed tomography image data and robotically drilled to the level of the facial recess. Intraoperative imaging was performed to confirm sufficient distance of the drilling trajectory to relevant anatomy. Robotic drilling continued toward the round window. The cochlear access was manually created by the surgeon. Electrode arrays were inserted through the keyhole tunnel under microscopic supervision via a tympanomeatal flap. All patients were successfully implanted with a cochlear implant. In 9 of 9 patients the robotic drilling was planned and performed to the level of the facial recess. In 3 patients, the procedure was reverted to a conventional approach for safety reasons. No change in facial nerve function compared to baseline measurements was observed. Robotic keyhole access for cochlear implantation is feasible. Further improvements to workflow complexity, duration of surgery, and usability including safety assessments are required to enable wider adoption of the procedure.

Deep learning methods outperform human capabilities in pattern recognition and data processing problems and now have an increasingly important role in scientific discovery. A key application of machine learning in molecular science is to learn potential energy surfaces or force fields from ab initio solutions of the electronic Schrödinger equation using data sets obtained with density functional theory, coupled cluster or other quantum chemistry (QC) methods. In this Review, we discuss a complementary approach using machine learning to aid the direct solution of QC problems from first principles. Specifically, we focus on quantum Monte Carlo methods that use neural-network ansatzes to solve the electronic Schrödinger equation, in first and second quantization, computing ground and excited states and generalizing over multiple nuclear configurations. Although still at their infancy, these methods can already generate virtually exact solutions of the electronic Schrödinger equation for small systems and rival advanced conventional QC methods for systems with up to a few dozen electrons. Quantum Monte Carlo methods using neutral-network ansatzes can provide virtually exact solutions to the electronic Schrödinger equations for small systems and are comparable to conventional quantum chemistry methods when investigating systems with dozens of electrons.

Accurate thermochemical data with sub-chemical accuracy (within 1 kcal mol of the empirical ground truth) are essential for advancing computational chemistry methods. However, existing datasets that reach this level of accuracy remain limited in size or scope. This hinders the development of data-driven methods with predictive accuracy across the broad chemical space of closed-shell, neutral molecules. Here we present Microsoft Research Accurate Chemistry Collection (MSR-ACC) and its first release, MSR-ACC/TAE25, comprising 73,040 total atomization energies at the CCSD(T)/CBS level obtained with the W1-F12 thermochemical protocol. The dataset is constructed to exhaustively cover the chemical space of closed-shell, charge-neutral, covalently bound equilibrium molecular structures containing up to 5 non-hydrogen atoms drawn from elements up to argon and lacking significant multireference character. The dataset and its canonical train and validation splits are openly available on Zenodo in the QCSchema format under the CDLA Permissive 2.0 license. This first release of MSR-ACC enables data-driven approaches for developing predictive computational chemistry methods with unprecedented accuracy and scope.

During robotic cochlear implantation a drill trajectory often passes at submillimeter distances from the facial nerve due to close lying critical anatomy of the temporal bone. Additional intraoperative safety mechanisms are thus required to ensure preservation of this vital structure in case of unexpected navigation system error. Electromyography based nerve monitoring is widely used to aid surgeons in localizing vital nerve structures at risk of injury during surgery. However, state of the art neuromonitoring systems, are unable to discriminate facial nerve proximity within submillimeter ranges. Previous work demonstrated the feasibility of utilizing combinations of monopolar and bipolar stimulation threshold measurements to discretize facial nerve proximity with greater sensitivity and specificity, enabling discrimination between safe (> 0.4 mm) and unsafe (< 0.1 mm) trajectories during robotic cochlear implantation (in vivo animal model). Herein, initial clinical validation of the determined stimulation protocol and nerve proximity analysis integrated into an image guided system for safety measurement is presented. Stimulation thresholds and corresponding nerve proximity values previously determined from an animal model have been validated in a first-in-man clinical trial of robotic cochlear implantation. Measurements performed automatically at preoperatively defined distances from the facial nerve were used to determine safety of the drill trajectory intraoperatively. The presented system and automated analysis correctly determined sufficient safety distance margins (> 0.4 mm) to the facial nerve in all cases.

Purpose A recent clinical trial has shown the feasibility of robotic cochlear implantation. The electrode was inserted through the robotically drilled tunnel and an additional access through the external auditory canal was created to provide for means of visualization and manipulation. To obviate the need for this additional access, the utilization of multiple robotically drilled tunnels targeting the round window has been proposed. The objective of this study was to assess the feasibility of electrode insertion through a robotic multiport approach. Methods In four ex vivo human head specimens (left side), four trajectories through the facial recess (2x) and the retrofacial and suprameatal region were planned and robotically drilled. Optimal three-port configurations were determined for each specimen by analyzing combinations of three of the four trajectories, where the three trajectories were used for the electrode, endoscopic visualization and manipulative assistance. Finally, electrode insertions were conducted through the optimal configurations. Results The electrodes could successfully be inserted, and the procedure sufficiently visualized through the facial recess drill tunnels in all specimens. Effective manipulative assistance for sealing the round window could be provided through the retrofacial tunnel. Conclusions Electrode insertion through a robotic three-port approach is feasible. Drill tunnels through the facial recess for the electrode and endoscope allow for optimized insertion angles and sufficient visualization. Through a retrofacial tunnel effective manipulation for sealing is possible.

Non-covalent π-π interactions are central to chemical and biological processes, yet the full understanding of their origin that would unite the simplicity of empirical approaches with the accuracy of quantum calculations is still missing. Here we employ a quantum-mechanical Hamiltonian model for van der Waals interactions, to demonstrate that intermolecular electron correlation in large supramolecular complexes at equilibrium distances is appropriately described by collective charge fluctuations. We visualize these fluctuations and provide connections both to orbital-based approaches to electron correlation, as well as to the simple London pairwise picture. The reported binding energies of ten supramolecular complexes obtained from the quantum-mechanical fluctuation model joined with density functional calculations are within 5% of the reference energies calculated with the diffusion quantum Monte-Carlo method. Our analysis suggests that π-π stacking in supramolecular complexes can be characterized by strong contributions to the binding energy from delocalized, collective charge fluctuations--in contrast to complexes with other types of bonding.

Influenza viruses (IVs) cause pneumonia in humans with progression to lung failure. Pulmonary DCs are key players in the antiviral immune response, which is crucial to restore alveolar barrier function. The mechanisms of expansion and activation of pulmonary DC populations in lung infection remain widely elusive. Using mouse BM chimeric and cell-specific depletion approaches, we demonstrated that alveolar epithelial cell (AEC) GM-CSF mediates recovery from IV-induced injury by affecting lung DC function. Epithelial GM-CSF induced the recruitment of CD11b+ and monocyte-derived DCs. GM-CSF was also required for the presence of CD103+ DCs in the lung parenchyma at baseline and for their sufficient activation and migration to the draining mediastinal lymph nodes (MLNs) during IV infection. These activated CD103+ DCs were indispensable for sufficient clearance of IVs by CD8+ T cells and for recovery from IV-induced lung injury. Moreover, GM-CSF applied intratracheally activated CD103+ DCs, inducing increased migration to MLNs, enhanced viral clearance, and attenuated lung injury. Together, our data reveal that GM-CSF-dependent cross-talk between IV-infected AECs and CD103+ DCs is crucial for effective viral clearance and recovery from injury, which has potential implications for GM-CSF treatment in severe IV pneumonia.