2 research outputs found
Toward Human-AI Co-creation to Accelerate Material Discovery
There is an increasing need in our society to achieve faster advances in
Science to tackle urgent problems, such as climate changes, environmental
hazards, sustainable energy systems, pandemics, among others. In certain
domains like chemistry, scientific discovery carries the extra burden of
assessing risks of the proposed novel solutions before moving to the
experimental stage. Despite several recent advances in Machine Learning and AI
to address some of these challenges, there is still a gap in technologies to
support end-to-end discovery applications, integrating the myriad of available
technologies into a coherent, orchestrated, yet flexible discovery process.
Such applications need to handle complex knowledge management at scale,
enabling knowledge consumption and production in a timely and efficient way for
subject matter experts (SMEs). Furthermore, the discovery of novel functional
materials strongly relies on the development of exploration strategies in the
chemical space. For instance, generative models have gained attention within
the scientific community due to their ability to generate enormous volumes of
novel molecules across material domains. These models exhibit extreme
creativity that often translates in low viability of the generated candidates.
In this work, we propose a workbench framework that aims at enabling the
human-AI co-creation to reduce the time until the first discovery and the
opportunity costs involved. This framework relies on a knowledge base with
domain and process knowledge, and user-interaction components to acquire
knowledge and advise the SMEs. Currently,the framework supports four main
activities: generative modeling, dataset triage, molecule adjudication, and
risk assessment.Comment: 9 pages, 5 figures, NeurIPS 2022 WS: AI4Scienc
Algoritmo evolucionário para otimização do plano de tratamento em radioterapia conformal 3D
O planejamento do tratamento por radioterapia tem por objetivo atingir um volume alvo com altas doses de radiação tomando cuidado para não expor órgãos sadios a doses elevadas. É, portanto, muito importante que se encontre um balanço ideal entre esses objetivos conflitantes. O presente trabalho relata um modelo de programação matemática multiobjetivo e introduz um Algoritmo Transgenético para o problema de seleção do direcionamento dos feixes de radiação no planejamento em radioterapia conformal 3D. A seleção das direções dos feixes é feita através de uma técnica denominada de isocentros variáveis. Com a finalidade de testar o potencial do algoritmo desenvolvido, realiza-se um experimento comparativo com um Algoritmo Genético Multiobjetivo. O experimento computacional obtém dados quantitativos e qualitativos que são analisados no trabalho.<br>The radiotherapy treatment planning aims to achieve a target volume with high doses of radiation taking care not to expose healthy organs to high doses. It is therefore very important to find an optimal balance between these conflicting goals. This paper reports a mathematical model of multiobjective programming and presents a Transgenetic Algorithm for the problem of selecting the direction of radiation beams in 3D conformal radiotherapy planning. The selection of beams directions is done with a technique called variable isocenters. In order to test the potential of the developed algorithm, a comparative experiment with a multiobjective genetic algorithm was done. The computational experiment obtains quantitative and qualitative data that are analyzed in this paper