Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
Machine-learning-guided directed evolution for protein engineering
Protein engineering through machine-learning-guided directed evolution enables the
optimization of protein functions. Machine-learning approaches predict how sequence maps …
optimization of protein functions. Machine-learning approaches predict how sequence maps …
Accurate global machine learning force fields for molecules with hundreds of atoms
Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …
[图书][B] Surrogates: Gaussian process modeling, design, and optimization for the applied sciences
RB Gramacy - 2020 - taylorfrancis.com
Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
When Gaussian process meets big data: A review of scalable GPs
The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …
hardware encourages success stories in the machine learning community. In the …
An optimization-centric view on Bayes' rule: Reviewing and generalizing variational inference
We advocate an optimization-centric view of Bayesian inference. Our inspiration is the
representation of Bayes' rule as infinite-dimensional optimization (Csisz´ r, 1975; Donsker …
representation of Bayes' rule as infinite-dimensional optimization (Csisz´ r, 1975; Donsker …
Data‐Driven Design for Metamaterials and Multiscale Systems: A Review
Metamaterials are artificial materials designed to exhibit effective material parameters that
go beyond those found in nature. Composed of unit cells with rich designability that are …
go beyond those found in nature. Composed of unit cells with rich designability that are …
A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization
Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
Efficiently sampling functions from Gaussian process posteriors
Gaussian processes are the gold standard for many real-world modeling problems,
especially in cases where a model's success hinges upon its ability to faithfully represent …
especially in cases where a model's success hinges upon its ability to faithfully represent …
Kernel methods through the roof: handling billions of points efficiently
Kernel methods provide an elegant and principled approach to nonparametric learning, but
so far could hardly be used in large scale problems, since naïve implementations scale …
so far could hardly be used in large scale problems, since naïve implementations scale …