J Chem Theory Comput
November 2024
Second-order Møller-Plesset perturbation theory (MP2) using the Resolution of the Identity approximation (RI-MP2) is a widely used method for computing molecular energies beyond the Hartree-Fock mean-field approximation. However, its high computational cost and lack of efficient algorithms for modern supercomputing architectures limit its applicability to large molecules. In this paper, we present the first distributed-memory many-GPU RI-MP2 algorithm explicitly designed to utilize hundreds of GPU accelerators for every step of the computation.
View Article and Find Full Text PDFBackground: Sharing trial results with participants is a moral imperative, but too often does not happen in appropriate ways.
Methods: We carried out semi-structured interviews with patients (n = 13) and site staff (n = 11), and surveyed 180 patients and 68 site staff who were part of the Show RESPECT study, which tested approaches to sharing results with participants in the context of the ICON8 ovarian cancer trial (ISRCTN10356387). Qualitative and free-text data were analysed thematically, and findings used to develop the SHOW RESPECT adaptable framework of considerations for planning how to share trial results with participants.
Objectives: This study aimed to understand the role of surgical Trainee Research Collaboratives (TRCs) in conducting randomised controlled trials and identify strategies to enhance trainee engagement in trials.
Design: This is a mixed methods study. We used observation of TRC meetings, semi-structured interviews and an online survey to explore trainees' motivations for engagement in trials and TRCs, including barriers and facilitators.
Background/aims: Sharing trial results with participants is an ethical imperative but often does not happen. Show RESPECT (ISRCTN96189403) tested ways of sharing results with participants in an ovarian cancer trial (ISRCTN10356387). Sharing results via a printed summary improved patient satisfaction.
View Article and Find Full Text PDFElectronic structure calculations have the potential to predict key matter transformations for applications of strategic technological importance, from drug discovery to material science and catalysis. However, a predictive physicochemical characterization of these processes often requires accurate quantum chemical modeling of complex molecular systems with hundreds to thousands of atoms. Due to the computationally demanding nature of electronic structure calculations and the complexity of modern high-performance computing hardware, quantum chemistry software has historically failed to operate at such large molecular scales with accuracy and speed that are useful in practice.
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