133,907 research outputs found
Alchemical normal modes unify chemical space
In silico design of new molecules and materials with desirable quantum
properties by high-throughput screening is a major challenge due to the high
dimensionality of chemical space. To facilitate its navigation, we present a
unification of coordinate and composition space in terms of alchemical normal
modes (ANMs) which result from second order perturbation theory. ANMs assume a
predominantly smooth nature of chemical space and form a basis in which new
compounds can be expanded and identified. We showcase the use of ANMs for the
energetics of the iso-electronic series of diatomics with 14 electrons, BN
doped benzene derivatives (C(BN)H with ),
predictions for over 1.8 million BN doped coronene derivatives, and genetic
energy optimizations in the entire BN doped coronene space. Using Ge lattice
scans as reference, the applicability ANMs across the periodic table is
demonstrated for III-V and IV-IV-semiconductors Si, Sn, SiGe, SnGe, SiSn, as
well as AlP, AlAs, AlSb, GaP, GaAs, GaSb, InP, InAs, and InSb. Analysis of our
results indicates simple qualitative structure property rules for estimating
energetic rankings among isomers. Useful quantitative estimates can also be
obtained when few atoms are changed to neighboring or lower lying elements in
the periodic table. The quality of the predictions often increases with the
symmetry of system chosen as reference due to cancellation of odd order terms.
Rooted in perturbation theory the ANM approach promises to generally enable
unbiased compound exploration campaigns at reduced computational cost
A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems
We develop a method for the evaluation of extreme event statistics associated
with nonlinear dynamical systems, using a small number of samples. From an
initial dataset of design points, we formulate a sequential strategy that
provides the 'next-best' data point (set of parameters) that when evaluated
results in improved estimates of the probability density function (pdf) for a
scalar quantity of interest. The approach utilizes Gaussian process regression
to perform Bayesian inference on the parameter-to-observation map describing
the quantity of interest. We then approximate the desired pdf along with
uncertainty bounds utilizing the posterior distribution of the inferred map.
The 'next-best' design point is sequentially determined through an optimization
procedure that selects the point in parameter space that maximally reduces
uncertainty between the estimated bounds of the pdf prediction. Since the
optimization process utilizes only information from the inferred map it has
minimal computational cost. Moreover, the special form of the metric emphasizes
the tails of the pdf. The method is practical for systems where the
dimensionality of the parameter space is of moderate size, i.e. order O(10). We
apply the method to estimate the extreme event statistics for a very
high-dimensional system with millions of degrees of freedom: an offshore
platform subjected to three-dimensional irregular waves. It is demonstrated
that the developed approach can accurately determine the extreme event
statistics using limited number of samples
Machine Learning, Quantum Mechanics, and Chemical Compound Space
We review recent studies dealing with the generation of machine learning
models of molecular and solid properties. The models are trained and validated
using standard quantum chemistry results obtained for organic molecules and
materials selected from chemical space at random
Machine learning reveals orbital interaction in crystalline materials
We propose a novel representation of crystalline materials named
orbital-field matrix (OFM) based on the distribution of valence shell
electrons. We demonstrate that this new representation can be highly useful in
mining material data. Our experiment shows that the formation energies of
crystalline materials, the atomization energies of molecular materials, and the
local magnetic moments of the constituent atoms in transition metal--rare-earth
metal bimetal alloys can be predicted with high accuracy using the OFM.
Knowledge regarding the role of coordination numbers of transition-metal and
rare-earth metal elements in determining the local magnetic moment of
transition metal sites can be acquired directly from decision tree regression
analyses using the OFM.Comment: 10 page
Integrated system to perform surrogate based aerodynamic optimisation for high-lift airfoil
This work deals with the aerodynamics optimisation of a generic two-dimensional three element high-lift configuration. Although the high-lift system is applied only during take-off and landing in the low speed phase of the flight the cost efficiency of the airplane is strongly influenced by it [1]. The ultimate goal of an aircraft high lift system design team is to define the simplest configuration which, for prescribed constraints, will meet the take-off, climb, and landing requirements usually expressed in terms of maximum L/D and/or maximum CL. The ability of the calculation method to accurately predict changes in objective function value when gaps, overlaps and element deflections are varied is therefore critical. Despite advances in computer capacity, the enormous computational cost of running complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of design optimisation. To cut down the cost, surrogate models, also known as metamodels, are constructed from and then used in place of the actual simulation models. This work outlines the development of integrated systems to perform aerodynamics multi-objective optimisation for a three-element airfoil test case in high lift configuration, making use of surrogate models available in MACROS Generic Tools, which has been integrated in our design tool. Different metamodeling techniques have been compared based on multiple performance criteria. With MACROS is possible performing either optimisation of the model built with predefined training sample (GSO) or Iterative Surrogate-Based Optimization (SBO). In this first case the model is build independent from the optimisation and then use it as a black box in the optimisation process. In the second case is needed to provide the possibility to call CFD code from the optimisation process, and there is no need to build any model, it is being built internally during the optimisation process. Both approaches have been applied. A detailed analysis of the integrated design system, the methods as well as th
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Generative Adversarial Networks (GANs) is a novel class of deep generative
models which has recently gained significant attention. GANs learns complex and
high-dimensional distributions implicitly over images, audio, and data.
However, there exists major challenges in training of GANs, i.e., mode
collapse, non-convergence and instability, due to inappropriate design of
network architecture, use of objective function and selection of optimization
algorithm. Recently, to address these challenges, several solutions for better
design and optimization of GANs have been investigated based on techniques of
re-engineered network architectures, new objective functions and alternative
optimization algorithms. To the best of our knowledge, there is no existing
survey that has particularly focused on broad and systematic developments of
these solutions. In this study, we perform a comprehensive survey of the
advancements in GANs design and optimization solutions proposed to handle GANs
challenges. We first identify key research issues within each design and
optimization technique and then propose a new taxonomy to structure solutions
by key research issues. In accordance with the taxonomy, we provide a detailed
discussion on different GANs variants proposed within each solution and their
relationships. Finally, based on the insights gained, we present the promising
research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table
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