Hard and soft materials: Putting consistent van der Waals density functionals to work

We present the idea and illustrate potential benefits of having a tool chain of closely related regular, unscreened and screened hybrid exchange-correlation (XC) functionals, all within the consistent formulation of the van der Waals density functional (vdW-DF) method [JPCM 32, 393001 (2020)]. Use of this chain of nonempirical XC functionals allows us to map when the inclusion of truly nonlocal exchange and of truly nonlocal correlation is important. Here we begin the mapping by addressing hard and soft material challenges: magnetic elements, perovskites, and biomolecular problems. We also predict the structure and polarization for a ferroelectric polymer. To facilitate this work and future broader explorations, we present a stress formulation for spin vdW-DF and illustrate the use of a simple stability-modeling scheme. The modeling supplements DFT (with a specific XC functional) by asserting whether the finding of a soft mode (an imaginary-frequency vibrational mode, ubiquitous in perovskites and soft matter) implies an actual DFT-based prediction of a low-temperature transformation.

Use of Stability Modeling to Support Accelerated Vaccine Development and Supply

Stability assessment of pharmaceuticals in specific storage and shipment conditions is a key requirement to ensure that safe and efficacious products are administered to patients. This is particularly relevant for vaccines, with numerous vaccines strictly requiring cold storage to remain stable. When stability evaluation is exclusively based on real-time data, it may represent a bottleneck for rapid and effective vaccine access. Stability modeling for vaccines represents a key resource to predict stability based on accelerated stability studies; nevertheless, this approach is not fully exploited for these kinds of products. This is likely because of the complexity and diversity of vaccines, as well as the limited availability of dedicated guidelines or illustrative case studies. This article reports a cross-company perspective on stability modeling for vaccines. Several examples, based on the direct experience of the contributors, demonstrate that modeling approaches can be highly valuable to predict vaccines’ shelf life and behavior during shipment or manipulation. It is demonstrated that modeling methodologies need to be tailored to the nature of the vaccine, the available prior knowledge, and the monitored attributes. Considering that the well-established strategies reported in ICH or WHO guidelines are not always broadly applicable to vaccines, this article represents an important source of information for vaccine researchers and manufacturers, setting the grounds for further discussion within the vaccine industry and with regulators.

Sensitivity of Neuromechanical Predictions to Choice of Glenohumeral Stability Modeling Approach

Most upper-extremity musculoskeletal models represent the glenohumeral joint with an inherently stable ball-and-socket, but the physiological joint requires active muscle coordination for stability. The authors evaluated sensitivity of common predicted outcomes (instability, net glenohumeral reaction force, and rotator cuff activations) to different implementations of active stabilizing mechanisms (constraining net joint reaction direction and incorporating normalized surface electromyography [EMG]). Both EMG and reaction force constraints successfully reduced joint instability. For flexion, incorporating any normalized surface EMG data reduced predicted instability by 54.8%, whereas incorporating any force constraint reduced predicted instability by 43.1%. Other outcomes were sensitive to EMG constraints, but not to force constraints. For flexion, incorporating normalized surface EMG data increased predicted magnitudes of joint reaction force and rotator cuff activations by 28.7% and 88.4%, respectively. Force constraints had no influence on these predicted outcomes for all tasks evaluated. More restrictive EMG constraints also tended to overconstrain the model, making it challenging to accurately track input kinematics. Therefore, force constraints may be a more robust choice when representing stability.