78 research outputs found
Representations of Materials for Machine Learning
High-throughput data generation methods and machine learning (ML) algorithms
have given rise to a new era of computational materials science by learning
relationships among composition, structure, and properties and by exploiting
such relations for design. However, to build these connections, materials data
must be translated into a numerical form, called a representation, that can be
processed by a machine learning model. Datasets in materials science vary in
format (ranging from images to spectra), size, and fidelity. Predictive models
vary in scope and property of interests. Here, we review context-dependent
strategies for constructing representations that enable the use of materials as
inputs or outputs of machine learning models. Furthermore, we discuss how
modern ML techniques can learn representations from data and transfer chemical
and physical information between tasks. Finally, we outline high-impact
questions that have not been fully resolved and thus, require further
investigation.Comment: 20 pages, 5 figures, To Appear in Annual Review of Materials Research
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SG-VAE: Scene Grammar Variational Autoencoder to Generate New Indoor Scenes
Deep generative models have been used in recent years to learn coherent latent representations in order to synthesize high-quality images. In this work, we propose a neural network to learn a generative model for sampling consistent indoor scene layouts. Our method learns the co-occurrences, and appearance parameters such as shape and pose, for different objects categories through a grammar-based auto-encoder, resulting in a compact and accurate representation for scene layouts. In contrast to existing grammar-based methods with a user-specified grammar, we construct the grammar automatically by extracting a set of production rules on reasoning about object co-occurrences in training data. The extracted grammar is able to represent a scene by an augmented parse tree. The proposed auto-encoder encodes these parse trees to a latent code, and decodes the latent code to a parse tree, thereby ensuring the generated scene is always valid. We experimentally demonstrate that the proposed auto-encoder learns not only to generate valid scenes (i.e. the arrangements and appearances of objects), but it also learns coherent latent representations where nearby latent samples decode to similar scene outputs. The obtained generative model is applicable to several computer vision tasks such as 3D pose and layout estimation from RGB-D data
SG-VAE: Scene Grammar Variational Autoencoder to generate new indoor scenes
Deep generative models have been used in recent years to learn coherent
latent representations in order to synthesize high-quality images. In this
work, we propose a neural network to learn a generative model for sampling
consistent indoor scene layouts. Our method learns the co-occurrences, and
appearance parameters such as shape and pose, for different objects categories
through a grammar-based auto-encoder, resulting in a compact and accurate
representation for scene layouts. In contrast to existing grammar-based methods
with a user-specified grammar, we construct the grammar automatically by
extracting a set of production rules on reasoning about object co-occurrences
in training data. The extracted grammar is able to represent a scene by an
augmented parse tree. The proposed auto-encoder encodes these parse trees to a
latent code, and decodes the latent code to a parse tree, thereby ensuring the
generated scene is always valid. We experimentally demonstrate that the
proposed auto-encoder learns not only to generate valid scenes (i.e. the
arrangements and appearances of objects), but it also learns coherent latent
representations where nearby latent samples decode to similar scene outputs.
The obtained generative model is applicable to several computer vision tasks
such as 3D pose and layout estimation from RGB-D data
A New Integer Linear Programming Formulation to the Inverse QSAR/QSPR for Acyclic Chemical Compounds Using Skeleton Trees
33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Kitakyushu, Japan, September 22-25, 2020.Computer-aided drug design is one of important application areas of intelligent systems. Recently a novel method has been proposed for inverse QSAR/QSPR using both artificial neural networks (ANN) and mixed integer linear programming (MILP), where inverse QSAR/QSPR is a major approach for drug design. This method consists of two phases: In the first phase, a feature function f is defined so that each chemical compound G is converted into a vector f(G) of several descriptors of G, and a prediction function ψ is constructed with an ANN so that ψ(f(G)) takes a value nearly equal to a given chemical property π for many chemical compounds G in a data set. In the second phase, given a target value y∗ of the chemical property π , a chemical structure G∗ is inferred in the following way. An MILP M is formulated so that M admits a feasible solution (x∗, y∗) if and only if there exist vectors x∗, y∗ and a chemical compound G∗ such that ψ(x∗)=y∗ and f(G∗)=x∗. The method has been implemented for inferring acyclic chemical compounds. In this paper, we propose a new MILP for inferring acyclic chemical compounds by introducing a novel concept, skeleton tree, and conducted computational experiments. The results suggest that the proposed method outperforms the existing method when the diameter of graphs is up to around 6 to 8. For an instance for inferring acyclic chemical compounds with 38 non-hydrogen atoms from C, O and S and diameter 6, our method was 5×104 times faster
Machine learning for molecular and materials science
Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.</p
Vibration-assisted resonance in photosynthetic excitation-energy transfer
Understanding how the effectiveness of natural photosynthetic energy-harvesting systems arises from the interplay between quantum coherence and environmental noise represents a significant challenge for quantum theory. Recently it has begun to be appreciated that discrete molecular vibrational modes may play an important role in the dynamics of such systems. Here we present a microscopic mechanism by which intramolecular vibrations may be able to contribute to the efficiency and directionality of energy transfer. Excited vibrational states create resonant pathways through the system, supporting fast and efficient energy transport. Vibrational damping together with the natural downhill arrangement of molecular energy levels gives intrinsic directionality to the energy flow. Analytical and numerical results demonstrate a significant enhancement of the efficiency and directionality of energy transport that can be directly related to the existence of resonances between vibrational and excitonic levels
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