Human-AI Co-Creation Approach to Find Forever Chemicals Replacements

Generative models are a powerful tool in AI for material discovery. We are designing a software framework that supports a human-AI co-creation process to accelerate finding replacements for the ``forever chemicals''-- chemicals that enable our modern lives, but are harmful to the environment and the human health. Our approach combines AI capabilities with the domain-specific tacit knowledge of subject matter experts to accelerate the material discovery. Our co-creation process starts with the interaction between the subject matter experts and a generative model that can generate new molecule designs. In this position paper, we discuss our hypothesis that these subject matter experts can benefit from a more iterative interaction with the generative model, asking for smaller samples and ``guiding'' the exploration of the discovery space with their knowledge.Comment: 5 pages, Generative AI and HCI (GenAICHI) Workshop at CHI 23 (ACM CHI Conference on Human Factors in Computing Systems

Image generation techniques using generative adversarial networks

GANs were first described in a year 2014, which is quite recently for algorithms. Although, during its time of existence, lots of various modifications and areas of possible usage were found. One of such area is image generation sphere, in which this algorithm is able to achieve results that, in some cases, do not differ from pictures drawn by a person or photographs of certain objects

Image generations techniques using Generative adversarial networks

The object of research is image generation algorithms based on GAN. The article reviews the main uses of these networks for image generation and main types of such algorithms, which can be used for this. Generative Adversarial Networks (GANs) have been a significant breakthrough in machine learning, allowing the generation of images that are indistinguishable from those created by humans. Although GANs have only been around since 2014, there get significant improvements due to changes in algorithms, usage of bigger datasets, and increase of computing power over the past eight years, resulting in various modifications of the network that are actively used today. Generally, all GANs can be divided into four main categories: Conditional GAN (CGAN), Progressive GAN (PGAN), StyleGAN, and CycleGAN, which are used for different tasks and cover most of the use cases of described algorithm. The GAN model consists of two main parts: a generator and a discriminator. The generator creates new instances from input data in the latent space, while the discriminator determines whether the instances are real or fake. Both models are trained based on the predictions of the discriminator, while coefficients are changed based on the MinMax algorithm. After that, some of the main modifications, such as StyleSwin, CWGAN, Layered Recursive GAN and CVAE-GAN were described, They can be used to improve the model and its main parameters such as the learning speed of the model, the quality of the obtained result and the number of artifacts that can appear during its operation. Ref. 13, pic.

Bayesian Reasoning with Trained Neural Networks

We showed how to use trained neural networks to perform Bayesian reasoning in order to solve tasks outside their initial scope. Deep generative models provide prior knowledge, and classification/regression networks impose constraints. The tasks at hand were formulated as Bayesian inference problems, which we approximately solved through variational or sampling techniques. The approach built on top of already trained networks, and the addressable questions grew super-exponentially with the number of available networks. In its simplest form, the approach yielded conditional generative models. However, multiple simultaneous constraints constitute elaborate questions. We compared the approach to specifically trained generators, showed how to solve riddles, and demonstrated its compatibility with state-of-the-art architectures

CCDCGAN: Inverse design of crystal structures

Autonomous materials discovery with desired properties is one of the ultimate goals for modern materials science. Applying the deep learning techniques, we have developed a generative model which can predict distinct stable crystal structures by optimizing the formation energy in the latent space. It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backwards propagator, both with their own advantages. Applying the generative models on the binary Bi-Se system reveals that distinct crystal structures can be obtained covering the whole composition range, and the phases on the convex hull can be reproduced after the generated structures are fully relaxed to the equilibrium. The method can be extended to multicomponent systems for multi-objective optimization, which paves the way to achieve the inverse design of materials with optimal properties.Comment: 14 pages, 3 figure

Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art

Procedural content generation (PCG) can be applied to a wide variety of tasks in games, from narratives, levels and sounds, to trees and weapons. A large amount of game content is comprised of graphical assets, such as clouds, buildings or vegetation, that do not require gameplay function considerations. There is also a breadth of literature examining the procedural generation of such elements for purposes outside of games. The body of research, focused on specific methods for generating specific assets, provides a narrow view of the available possibilities. Hence, it is difficult to have a clear picture of all approaches and possibilities, with no guide for interested parties to discover possible methods and approaches for their needs, and no facility to guide them through each technique or approach to map out the process of using them. Therefore, a systematic literature review has been conducted, yielding 200 accepted papers. This paper explores state-of-the-art approaches to graphical asset generation, examining research from a wide range of applications, inside and outside of games. Informed by the literature, a conceptual framework has been derived to address the aforementioned gaps

Генерація аватарів за категоріями з використанням мережі GAN

Пояснювальна записка магістерської дисертації складається з п’яти розділів, містить 39 рисунків, 13 таблиць, 4 додатки та 45 джерел. Об`єкт дослідження: процес генерації зображень у вигляді аватарів у соціальні мережі за категоріями. Мета дипломного проекту: модифікація моделі GAN для її використання у генерації аватарів за певними категоріями. При цьому, підвищення її ефективності за рахунок зниження часу навчання моделі, не знижуючи якості отриманого зображення. Практична цінність даної роботи полягає у розробці моделі, що дозволяє генерувати аватари користувачів для використання на веб-ресурсах за запитами за категоріями.The explanatory note of the master’s dissertation consists of five sections, contains 39 pictures, 13 tables, 4 appendixes, 45 sources. The object of study: the process of generating images in the form of avatars in social media by categories. The aim of the diploma project: modification of the GAN model for its usage in a avatars generation process by specific categories. At the same time, improving its efficiency by reducing the speed of generation and model training without compromising the quality of the resulting image. The practical value of this work lies in the development of a model that allows to generate user avatars for further usage on web resources based on category based requests