5 research outputs found
Prediction of Adsorption in Nano-Pores with Graph Neural Networks
We investigate the graph-based convolutional neural network approach for
predicting and ranking gas adsorption properties of crystalline Metal-Organic
Framework (MOF) adsorbents for application in post-combustion capture of
. Our model is based solely on standard structural input files
containing atomistic descriptions of the adsorbent material candidates. We
construct novel methodological extensions to match the prediction accuracy of
classical machine learning models that were built with hundreds of features at
much higher computational cost. Our approach can be more broadly applied to
optimize gas capture processes at industrial scale.Comment: AAAI Conference on Artificial Intelligence (2022
Carbon Figures of Merit Knowledge Creation with a Hybrid Solution and Carbon Tables API
Nowadays there are algorithms, methods, and platforms that are being created
to accelerate the discovery of materials that are able to absorb or adsorb
molecules that are in the atmosphere or during the combustion in power
plants, for instance. In this work an asynchronous REST API is described to
accelerate the creation of Carbon figures of merit knowledge, called Carbon
Tables, because the knowledge is created from tables in scientific PDF
documents and stored in knowledge graphs. The figures of merit knowledge
creation solution uses a hybrid approach, in which heuristics and machine
learning are part of. As a result, one can search the knowledge with mature and
sophisticated cognitive tools, and create more with regards to Carbon figures
of merit
A Hybrid Architecture for Multi-Party Conversational Systems
Multi-party Conversational Systems are systems with natural language interaction between one or more people or systems. From the moment that an utterance is sent to a group, to the moment that it is replied in the group by a member, several activities must be done by the system: utterance understanding, information search, reasoning, among others. In this paper we present the challenges of designing and building multi-party conversational systems, the state of the art, our proposed hybrid architecture using both norms and machine learning and some insights after implementing and evaluating one on the finance domain