Understanding conformational dynamics from macromolecular crystal diffuse scattering

All macromolecular crystals contain some extent of disorder. The diffraction from such crystals contains diffuse scattering in addition to Bragg peaks and this scattering contains information about correlated displacements in the constituent molecules. While much work has been performed recently in decoding the dynamics of the crystalline ordering, the goal of understanding the internal dynamics of the molecules within a unit cell has been out-of-reach. In this article, we propose a general framework to extract the internal conformational modes of a macromolecule from diffuse scattering data. We combine insights on the distribution of diffuse scattering from short- and long-range disorder with a Bayesian global optimization algorithm to obtain the best fitting internal motion modes to the data. To illustrate the efficacy of the method, we apply it to a publicly available dataset from triclinic lysozyme. Our mostly parameter-free approach can enable the recovery of a much richer, dynamic structure from macromolecular crystallography.

Parallel Bayesian Global Optimization of Expensive Functions

Large-Scale Parallel Bayesian Optimization

Adaptive Dimensionality Reduction for Fast Sequential Optimization With Gaussian Processes

Available computational models for many engineering design applications are both expensive and and of a black-box nature. This renders traditional optimization techniques difficult to apply, including gradient-based optimization and expensive heuristic approaches. For such situations, Bayesian global optimization approaches, that both explore and exploit a true function while building a metamodel of it, are applied. These methods often rely on a set of alternative candidate designs over which a querying policy is designed to search. For even modestly high-dimensional problems, such an alternative set approach can be computationally intractable, due to the reliance on excessive exploration of the design space. To overcome this, we have developed a framework for the optimization of expensive black-box models, which is based on active subspace exploitation and a two-step knowledge gradient policy. We demonstrate our approach on three benchmark problems and a practical aerostructural wing design problem, where our method performs well against traditional direct application of Bayesian global optimization techniques.

Expensive Black-Box Model Optimization Via a Gold Rush Policy

The optimization of black-box models is a challenging task owing to the lack of analytic gradient information and structural information about the underlying function, and also due often to significant run times. A common approach to tackling such problems is the implementation of Bayesian global optimization techniques. However, these techniques often rely on surrogate modeling strategies that endow the approximation of the underlying expensive function with nonexistent features. Further, these techniques tend to push new queries away from previously queried design points, making it difficult to locate an optimum point that rests near a previous model evaluation. To overcome these issues, we propose a gold rush (GR) policy that relies on purely local information to identify the next best design alternative to query. The method employs a surrogate constructed pointwise, that adds no additional features to the approximation. The result is a policy that performs well in comparison to state of the art Bayesian global optimization methods on several benchmark problems. The policy is also demonstrated on a constrained optimization problem using a penalty method.

Expensive Black-Box Model Optimization via a Gold Rush Policy

The optimization of expensive black-box models is a challenging task owing to the lack of analytic gradient information and structural information about the underlying function, and also due to the sheer computational expense. A common approach to tackling such problems is the implementation of Bayesian global optimization techniques. However, these techniques often rely on surrogate modeling strategies that endow the approximation of the underlying expensive function with nonexistent features. Further, these techniques tend to push new queries away from previously queried design points, making it difficult to locate an optimum point that rests near a previous model evaluation. To overcome these issues, we propose a gold rush policy that relies on purely local information to identify the next best design alternative to query. The method employs a surrogate constructed pointwise, that adds no additional features to the approximation. The result is a policy that performs well in comparison to state of the art Bayesian global optimization methods on several benchmark problems. The policy is also demonstrated on a constrained optimization problem using a penalty method.