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"Smith, Justin"
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Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology, and chemistry, and billions of times faster than CCSD(T)/CBS calculations.
Computational modelling of chemical systems requires a balance between accuracy and computational cost. Here the authors use transfer learning to develop a general purpose neural network potential that approaches quantum-chemical accuracy for reaction thermochemistry, isomerization, and drug-like molecular torsions.
Journal Article
The Implementation Research Logic Model: a method for planning, executing, reporting, and synthesizing implementation projects
Background
Numerous models, frameworks, and theories exist for specific aspects of implementation research, including for determinants, strategies, and outcomes. However, implementation research projects often fail to provide a coherent rationale or justification for how these aspects are selected and tested in relation to one another. Despite this need to better specify the conceptual linkages between the core elements involved in projects, few tools or methods have been developed to aid in this task. The Implementation Research Logic Model (IRLM) was created for this purpose and to enhance the rigor and transparency of describing the often-complex processes of improving the adoption of evidence-based interventions in healthcare delivery systems.
Methods
The IRLM structure and guiding principles were developed through a series of preliminary activities with multiple investigators representing diverse implementation research projects in terms of contexts, research designs, and implementation strategies being evaluated. The utility of the IRLM was evaluated in the course of a 2-day training to over 130 implementation researchers and healthcare delivery system partners.
Results
Preliminary work with the IRLM produced a core structure and multiple variations for common implementation research designs and situations, as well as guiding principles and suggestions for use. Results of the survey indicated a high utility of the IRLM for multiple purposes, such as improving rigor and reproducibility of projects; serving as a “roadmap” for how the project is to be carried out; clearly reporting and specifying how the project is to be conducted; and understanding the connections between determinants, strategies, mechanisms, and outcomes for their project.
Conclusions
The IRLM is a semi-structured, principle-guided tool designed to improve the specification, rigor, reproducibility, and testable causal pathways involved in implementation research projects. The IRLM can also aid implementation researchers and implementation partners in the planning and execution of practice change initiatives. Adaptation and refinement of the IRLM are ongoing, as is the development of resources for use and applications to diverse projects, to address the challenges of this complex scientific field.
Journal Article
Divine machines : Leibniz and the sciences of life
\"Though it did not yet exist as a discrete field of scientific inquiry, biology was at the heart of many of the most important debates in seventeenth-century philosophy. Nowhere is this more apparent than in the work of G. W. Leibniz. In Divine Machines, Justin Smith offers the first in-depth examination of Leibniz's deep and complex engagement with the empirical life sciences of his day, in areas as diverse as medicine, physiology, taxonomy, generation theory, and paleontology. He shows how these wide-ranging pursuits were not only central to Leibniz's philosophical interests, but often provided the insights that led to some of his best-known philosophical doctrines.Presenting the clearest picture yet of the scope of Leibniz's theoretical interest in the life sciences, Divine Machines takes seriously the philosopher's own repeated claims that the world must be understood in fundamentally biological terms. Here Smith reveals a thinker who was immersed in the sciences of life, and looked to the living world for answers to vexing metaphysical problems. He casts Leibniz's philosophy in an entirely new light, demonstrating how it radically departed from the prevailing models of mechanical philosophy and had an enduring influence on the history and development of the life sciences. Along the way, Smith provides a fascinating glimpse into early modern debates about the nature and origins of organic life, and into how philosophers such as Leibniz engaged with the scientific dilemmas of their era\"-- Provided by publisher.
BOOK
Teaching a neural network to attach and detach electrons from molecules
Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. Most DNN potentials were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we propose an improved machine learning framework for simulating open-shell anions and cations. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity with errors of about 2–3 kcal/mol and spin-charges with error errors ~0.01e for small and medium-sized organic molecules, compared to the reference QM simulations. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions.
Quantum mechanical calculations of molecular ionized states are computationally quite expensive. This work reports a successful extension of a previous deep-neural networks approach towards transferable neural-network models for predicting multiple properties of open shell anions and cations.
Journal Article
Irrationality : a history of the dark side of reason
\"What every leader needs to know about dignity and how to create a culture in which everyone thrives. This landmark book from an expert in dignity studies explores the essential but under-recognized role of dignity as part of good leadership. Extending the reach of her award-winning book Dignity: Its Essential Role in Resolving Conflict, Donna Hicks now contributes a specific, practical guide to achieving a culture of dignity. Most people know very little about dignity, the author has found, and when leaders fail to respect the dignity of others, conflict and distrust ensue. She highlights three components of leading with dignity: what one must know in order to honor dignity and avoid violating it; what one must do to lead with dignity; and how one can create a culture of dignity in any organization, whether corporate, religious, governmental, healthcare, or beyond. Brimming with key research findings, real-life case studies, and workable recommendations, this book fills an important gap in our understanding of how best to be together in a conflict-ridden world.\"-- Publisher's description.
Book
Automated discovery of a robust interatomic potential for aluminum
Machine learning, trained on quantum mechanics (QM) calculations, is a powerful tool for modeling potential energy surfaces. A critical factor is the quality and diversity of the training dataset. Here we present a highly automated approach to dataset construction and demonstrate the method by building a potential for elemental aluminum (ANI-Al). In our active learning scheme, the ML potential under development is used to drive non-equilibrium molecular dynamics simulations with time-varying applied temperatures. Whenever a configuration is reached for which the ML uncertainty is large, new QM data is collected. The ML model is periodically retrained on all available QM data. The final ANI-Al potential makes very accurate predictions of radial distribution function in melt, liquid-solid coexistence curve, and crystal properties such as defect energies and barriers. We perform a 1.3M atom shock simulation and show that ANI-Al force predictions shine in their agreement with new reference DFT calculations.
The accuracy of a machine-learned potential is limited by the quality and diversity of the training dataset. Here the authors propose an active learning approach to automatically construct general purpose machine-learning potentials here demonstrated for the aluminum case.
Journal Article
Epidemiology and Socioeconomic Trends in Adult Spinal Deformity Care
Abstract
Adult spinal deformity (ASD) has gained significant attention over the past decade with improvements in diagnostic tools, classification schemes, and surgical technique. The demographics of the aging population in the United States are undergoing a fundamental shift as medical care advances and life expectancy increases. The “baby boomers” represent the fastest growing demographic in the United States and by 2050, the number of individuals 65 yr and older is projected to reach 89 million, more than double its current size. Based on current prevalence estimates there are approximately 27.5 million elderly individuals with some form of spinal deformity, which will place a significant burden on our health care systems. Rates of surgery for ASD and case complexity are both increasing, with concomitant increase in the cost of deformity care. At the same time, patients are more medically complex with increasing number of comorbidities that result in increased surgical risk and complication profiles. This review aims to highlight recent trends in the epidemiology and socioeconomic patterns in surgery for ASD.
Journal Article