43 research outputs found

    Embedded Mean-Field Theory for Solution-Phase Transition-Metal Polyolefin Catalysis

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    Decreasing the wall-clock time of quantum mechanics/molecular mechanics (QM/MM) calculations without sacrificing accuracy is a crucial prerequisite for widespread simulation of solution-phase dynamical processes. In this work, we demonstrate the use of embedded mean-field theory (EMFT) as the QM engine in QM/MM molecular dynamics (MD) simulations to examine polyolefin catalysts in solution. We show that employing EMFT in this mode preserves the accuracy of hybrid-functional DFT in the QM region, while providing up to 20-fold reductions in the cost per SCF cycle, thereby increasing the accessible simulation time-scales. We find that EMFT reproduces DFT-computed binding energies and optimized bond lengths to within chemical accuracy, as well as consistently ranking conformer stability. Furthermore, solution-phase EMFT/MM simulations provide insight into the interaction strength of strongly coordinating and bulky counterions

    Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces

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    We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-cost approximated quantum operators in the symmetry-adapted atomic orbital basis. The model is end-to-end differentiable due to the derivation of analytic gradients for all electronic structure terms, and is shown to be transferable across chemical space due to the use of domain-specific features. The learning efficiency is improved by incorporating physically motivated constraints on the electronic structure through multi-task learning. The model outperforms existing methods on energy prediction tasks for the QM9 dataset and for molecular geometry optimizations on conformer datasets, at a computational cost that is thousand-fold or more reduced compared to conventional quantum-chemistry calculations (such as density functional theory) that offer similar accuracy

    Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces

    Get PDF
    We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-cost approximated quantum operators in the symmetry-adapted atomic orbital basis. The model is end-to-end differentiable due to the derivation of analytic gradients for all electronic structure terms, and is shown to be transferable across chemical space due to the use of domain-specific features. The learning efficiency is improved by incorporating physically motivated constraints on the electronic structure through multi-task learning. The model outperforms existing methods on energy prediction tasks for the QM9 dataset and for molecular geometry optimizations on conformer datasets, at a computational cost that is thousand-fold or more reduced compared to conventional quantum-chemistry calculations (such as density functional theory) that offer similar accuracy

    entos: A Quantum Molecular Simulation Package

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    entos is designed for ab initio MD simulations of molecular and condensed-phase chemical reactions and other processes, with particular focus on mean-field and quantum embedding methods for electronic structure. The entos software package is developed in the C++14 programming language with a structure that enables flexibility (by providing a long-term sustainable platform for development of methods in this area), efficiency (via task-based multi-threaded parallelism), and rigorous software engineering standards

    entos: A Quantum Molecular Simulation Package

    Get PDF
    entos is designed for ab initio MD simulations of molecular and condensed-phase chemical reactions and other processes, with particular focus on mean-field and quantum embedding methods for electronic structure. The entos software package is developed in the C++14 programming language with a structure that enables flexibility (by providing a long-term sustainable platform for development of methods in this area), efficiency (via task-based multi-threaded parallelism), and rigorous software engineering standards

    Venture funding for science-based African health innovation

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    <p>Abstract</p> <p>Background</p> <p>While venture funding has been applied to biotechnology and health in high-income countries, it is still nascent in these fields in developing countries, and particularly in Africa. Yet the need for implementing innovative solutions to health challenges is greatest in Africa, with its enormous burden of communicable disease. Issues such as risk, investment opportunities, return on investment requirements, and quantifying health impact are critical in assessing venture capital’s potential for supporting health innovation. This paper uses lessons learned from five venture capital firms from Kenya, South Africa, China, India, and the US to suggest design principles for African health venture funds.</p> <p>Discussion</p> <p>The case study method was used to explore relevant funds, and lessons for the African context. The health venture funds in this study included publicly-owned organizations, corporations, social enterprises, and subsidiaries of foreign venture firms. The size and type of investments varied widely. The primary investor in four funds was the International Finance Corporation. Three of the funds aimed primarily for financial returns, one aimed primarily for social and health returns, and one had mixed aims. Lessons learned include the importance of measuring and supporting both social and financial returns; the need to engage both upstream capital such as government risk-funding and downstream capital from the private sector; and the existence of many challenges including difficulty of raising capital, low human resource capacity, regulatory barriers, and risky business environments. Based on these lessons, design principles for appropriate venture funding are suggested.</p> <p>Summary</p> <p>Based on the cases studied and relevant experiences elsewhere, there is a case for venture funding as one support mechanism for science-based African health innovation, with opportunities for risk-tolerant investors to make financial as well as social returns. Such funds should be structured to overcome the challenges identified, be sustainable in the long run, attract for-profit private sector funds, and have measurable and significant health impact. If this is done, the proposed venture approach may have complementary benefits to existing initiatives and encourage local scientific and economic development while tapping new sources of funding.</p

    Towards an Economy of Higher Education

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    This paper draws a distinction between ways thinking and acting, and hence of policy and practice in higher education, in terms of different kinds of economy: economies of exchange and economies of excess. Crucial features of economies of exchange are outlined and their presence in prevailing conceptions of teaching and learning is illustrated. These are contrasted with other possible forms of practice, which in turn bring to light the nature of an economy of excess. In more philosophical terms, and to expand on the picture, economies of excess are elaborated with reference, first, to the understanding of alterity in the work of Emmanuel Levinas and, second, to the idea of Dionysian intensity that is to be found in Nietzsche. In the light of critical comment on some current directions in policy and practice, the implications of these ways of thinking for the administrator, the teacher and the student in higher education are explored
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