75 skills found · Page 1 of 3
LopezGroup-ICIQ / CareAutomated creation and manipulation of Chemical Reaction Networks (CRNs) in heterogeneous catalysis, allowing the evaluation of species and reaction properties with data-driven ML models and the network simulation with microkinetic modelling.
alxfgh / Large Language Models In ChemistryWorking collection of papers, repos and models of transformer based language models trained or tuned for the Chemical domain, from natural language to chemical modeling and property prediction
pnnl / IsicleIn silico chemical library engine for high-accuracy chemical property prediction
ORNL-CEES / ThermochimicaComputational library for chemical thermodynamics and phase equilibrium calculation. Multiphysics and standalone estimations of chemical state and constitutive and transport properties.
srebughini / ASALIDo you work with chemical reactors? Are you curious about them? ASALI is the open-source code that you are looking for. Chemical reactor models, transport/thermodynamic properties of gases, equilibrium calculations. ASALI couples all these features with an user friendly graphical interface. Modeling catalytic reactors has never been so easy.
jinhojsk515 / SPMM[Nat. Comm. 2024] Multimodal learning for chemical domain, with SMILES and properties.
BiomedSciAI / Biomed Multi ViewThis repository contains the implementation of the Multi-view Molecular Embedding with Late Fusion (MMELON) architecture. MMELON combines molecular representations from three views — image, graph, and text —to learn a joint embedding that can be finetuned for downstream tasks in chemical and biological property prediction.
devalab / CIGINAAAI 2020: Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-like Molecules
Augmented-Nature / PubChem MCP ServerA comprehensive Model Context Protocol (MCP) server for accessing the PubChem chemical database. This server provides access to over 110 million chemical compounds with extensive molecular properties, bioassay data, and chemical informatics tools.
ecrl / GraphchemGraph-based machine learning for chemical property prediction
HzaCode / ChemInformant⚗️ An all-in-one solution for chemical property retrieval from PubChem.
chemcognition-lab / ChemixhubCheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction
swansonk14 / Chemprop IntroAn introduction to machine learning for chemical property prediction
CambridgeMolecularEngineering / ChemdataextractorPipeline for automated extraction of chemical property information from scientific documents
srijitseal / PKSmartPKSmart: Predicting PK properties using Chemical Structures
DrrDom / SpciTool for mining structure-property relationships from chemical datasets
jwoerner42 / LCW Fine Tuning ChemBERTa 2Demonstrator for the effectiveness of transformer models, specifically the newly released ChemBERTa-2, in predicting physical-chemical property endpoints with comparable accuracy to standard machine-learning techniques without the need for descriptor calculation and selection
Satya3720 / Rock Identification Using Deep Convolution Neural NetworkRocks are a fundamental component of Earth. The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. It is a basic part of geological surveying and research, and mineral resources exploration. The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. Working conditions in the field generally limit identification to visual methods, including using a magnifying glass for fine-grained rocks. Visual inspection assesses properties such as colour, composition, grain size, and structure. The attributes of rocks reflect their mineral and chemical composition, formation environment, and genesis. The colour of rock reflects its chemical composition. But these analysis is time taken process to identify the rocks.Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. Solution: Deep learning is receiving significant research attention for pattern recognition and machine learning. Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. The results show that the proposed approach based on deep learning represents an improvement in intelligent rock-type identification and solves several difficulties facing the automated identification of rock types in the field.Who are experienced in the field of geological they can identify the rocks easily. But who are new to the field, it can help to identify the type of rock.
evijit / DeepChemUsing Deep Learning to predict properties of Chemicals
MilesZhao / EcloudMaterials representation plays a key role in machine learning based prediction of materials properties and new materials discovery. Currently both graph and 3D voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor for materials property prediction with the advantage of possessing close relation with the physical and chemical properties of materials. We developed an ECD based 3D convolutional neural networks (CNNs) for predicting elastic properties of materials, in which CNNs can learn effective hierarchical features with multiple convolving and pooling operations. Extensive benchmark experiments over 2,170 Fm-3m face-centered-cubic (FCC) materials show that our ECD based CNNs can achieve good performance for elasticity prediction. Especially, our CNN models based on the fusion of elemental Magpie features and ECD descriptors achieved the best 5-fold cross-validation performance. More importantly, we showed that our ECD based CNN models can achieve significantly better extrapolation performance when evaluated over non-redundant datasets where there are few neighbor training samples around test samples. As additional validation, we evaluated the predictive performance of our models on 329 materials of space group Fm-3m by comparing to DFT calculated values, which shows better prediction power of our model for bulk modulus than shear modulus. Due to the unified representation power of ECD, it is expected that our ECD based CNN approach can also be applied to predict other physical and chemical properties of crystalline materials.
