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Found 13 papers, 4 papers with code
A Large Encoder-Decoder Family of Foundation Models For Chemical Language
These methods excel in tasks such as property prediction and molecule generation by learning contextualized representations of input tokens through self-supervised learning on large unlabeled corpora.
KIF: A Wikidata-Based Framework for Integrating Heterogeneous Knowledge Sources
We present a Wikidata-based framework, called KIF, for virtually integrating heterogeneous knowledge sources.
Improving Molecular Properties Prediction Through Latent Space Fusion
Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and sample efficiency.
Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction
Our approach opens up promising avenues for future research in molecular property prediction by harnessing the synergistic potential of both chemical language and physicochemical features, leading to enhanced performance and advancements in the field.
Human-AI Co-Creation Approach to Find Forever Chemicals Replacements
Generative models are a powerful tool in AI for material discovery.
Position Paper on Dataset Engineering to Accelerate Science
Moreover, the dataset will be used in most cases as a blueprint of an entity that at any moment can be stored as a table.
Knowledge-augmented Risk Assessment (KaRA): a hybrid-intelligence framework for supporting knowledge-intensive risk assessment of prospect candidates
Evaluating the potential of a prospective candidate is a common task in multiple decision-making processes in different industries.
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.
Workflow Provenance in the Lifecycle of Scientific Machine Learning
We contribute with (i) characterization of the lifecycle and taxonomy for data analyses; (ii) design principles to build this view, with a W3C PROV compliant data representation and a reference system architecture; and (iii) lessons learned after an evaluation in an Oil & Gas case using an HPC cluster with 393 nodes and 946 GPUs.
Managing Data Lineage of O&G Machine Learning Models: The Sweet Spot for Shale Use Case
Machine Learning (ML) has increased its role, becoming essential in several industries.
Effective Integration of Symbolic and Connectionist Approaches through a Hybrid Representation
In this paper, we present our position for a neuralsymbolic integration strategy, arguing in favor of a hybrid representation to promote an effective integration.
Managing Machine Learning Workflow Components
To handle this problem, in this paper, we introduce machine learning workflow management (MLWfM) as a technique to aid the development and reuse of MLWfs and their components through three aspects: representation, execution, and creation.
Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering
To handle this problem, in this paper we contribute with a detailed characterization of provenance data in the ML lifecycle in CSE; a new provenance data representation, called PROV-ML, built on top of W3C PROV and ML Schema; and extensions to a system that tracks provenance from multiple workflows to address the characteristics of ML and CSE, and to allow for provenance queries with a standard vocabulary.
