Machine Learning in Chemistry

@ML_Chem

Feed that aggregates publications about Machine Learning in Chemistry from over 200 journals of 15 editors. Contact @CYL_Lab for any question.

Sherbrooke, Canada

Joined August 2021

7Following

5KFollowers

Machine Learning in Chemistry@ML_Chem · 4 m

Robust Prediction of Protein–Ligand Binding Potency with Multi-Modal Customized Gate Control #machinelearning #compchem dx.doi.org/10.26434/chemr…

Machine Learning in Chemistry@ML_Chem · 4 m

Deep learning framework based on ITOC optimization for coal spontaneous combustion temperature prediction: a coupled CNN-BiGRU-CBAM model #machinelearning #compchem nature.com/articles/s4159…

ML_Chem's tweet card. Scientific Reports - Deep learning framework based on ITOC optimization for coal spontaneous combustion temperature prediction: a coupled CNN-BiGRU-CBAM model

Machine Learning in Chemistry@ML_Chem · 4 m

Protein–ligand data at scale to support machine learning #machinelearning #compchem nature.com/articles/s4157…

ML_Chem's tweet card. Nature Reviews Chemistry - Target 2035 aims to develop a potent and selective pharmacological modulator for every human protein by 2035 with the results made publicly available. This Roadmap...

Machine Learning in Chemistry@ML_Chem · 5 m

Machine Learning-Assisted Design of Molecular Structure of Diphenylamine Antioxidants #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

ML_Chem's tweet card. This study calculated the bond dissociation energy (BDE), solubility parameter (δ), binding energy (Ebinding), and other parameters of 96 diphenylamine antioxidants using molecular simulations to...

Machine Learning in Chemistry@ML_Chem · 2 h

Innovative Computational Framework for Sr3SbCl3 Absorber Optimization: DFT, SCAPS-1D, and Machine Learning Perspectives #machinelearning #compchem pubs.rsc.org/en/Content/Art…

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Machine Learning in Chemistry@ML_Chem · 2 h

High-throughput screening accelerated by machine learning for morphology of silica nanoparticles with high cell permeability #machinelearning #compchem pubs.rsc.org/en/Content/Art…

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Machine Learning in Chemistry@ML_Chem · 2 h

Uncertainty-Informed Screening for Safer Solvents Used in the Synthesis of Perovskites via Language Models #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

ML_Chem's tweet card. Automated data curation for niche scientific topics, where data quality and contextual accuracy are paramount, poses significant challenges. Bidirectional contextual models such as BERT and ELMo...

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Machine Learning in Chemistry@ML_Chem · 2 h

Applications of Machine Learning in High-Entropy Alloys: A Review of Recent Advances in Design, Discovery, and Characterization #machinelearning #compchem pubs.rsc.org/en/Content/Art…

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Machine Learning in Chemistry@ML_Chem · 16 h

Exploring Phase Stability and Transport Properties of Emerging Thermoelectric Materials: Machine Learning and Experimental Insights #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

ML_Chem's tweet card. This study investigates the thermoelectric (TE) properties of newly synthesized materials, including Heusler alloys, magnesium silicides, and tetrahedrites, identified through machine learning (ML)...

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Machine Learning in Chemistry@ML_Chem · 20 h

Structural Descriptor Bridging the Microstructural Feature and Catalytic Reactivity for Rational Design of Metal Catalysts #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

ML_Chem's tweet card. ConspectusMetal heterogeneous catalysis is the workhorse of the chemical industry, driving the conversion of reactants to desirable products. Traditional design approaches for metal catalysts rely on...

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Machine Learning in Chemistry@ML_Chem · 20 h

Stable Bipolar Resistive Switching in Lead-Free Cs2AgBiBr6 Memristors for Neuromorphic Computing #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

ML_Chem's tweet card. The lead-free double-perovskite Cs2AgBiBr6, which combines nontoxicity with superior stability, has been highlighted as an ecofriendly alternative to lead-halide perovskites in photoelectric devices...

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Machine Learning in Chemistry@ML_Chem · 20 h

I-GAT: Interpretable Graph Attention Networks for Ligand Optimization #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

ML_Chem's tweet card. Designing selective and potent ligands for target receptors remains a significant challenge in drug discovery. Computational approaches, particularly advancements in machine learning (ML), offer...

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Machine Learning in Chemistry@ML_Chem · 20 h

Cluster-Based Machine Learning Potentials to Describe Disordered Metal–Organic Frameworks up to the Mesoscale #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

ML_Chem's tweet card. Metal–organic frameworks (MOFs) are highly interesting and tunable materials. By incorporating spatial defects into their atomic structure, MOFs can be finetuned to exhibit precise chemical functio...

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Machine Learning in Chemistry@ML_Chem · 22 h

Machine Learning-Enhanced Single-Particle Tracking for Rapid Screening of Tumor Immunomodulatory Drugs #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

ML_Chem's tweet card. The tumor microenvironment plays a critical role in tumor progression and immune response, with the extracellular matrix (ECM) regulating immune cell infiltration. However, the interplay between ECM...

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Machine Learning in Chemistry@ML_Chem · 22 h

m5U-HybridNet: Integrating an RNA Foundation Model with CNN Features for Accurate Prediction of 5-Methyluridine Modification Sites #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

ML_Chem's tweet card. The 5-methyluridine (m5U) modification in RNA is vital for numerous biological processes, making its precise identification a key focus in computational biology. However, traditional wet-lab detect...

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Machine Learning in Chemistry@ML_Chem · 22 h

Dynamic Training Enhances Machine Learning Potentials for Long-Lasting Molecular Dynamics #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

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Machine Learning in Chemistry@ML_Chem · 22 h

Prediction of Fraction Unbound in Human Plasma for Per- and Polyfluoroalkyl Substances: Evaluating Transfer Learning as an Algorithmic Solution to the Problem of Sparse Data #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

$ML_Chem's tweet card. Fraction unbound in plasma (Fup) is a crucial parameter in physiologically based toxicokinetic (PBTK) models, representing the fraction of a chemical compound that is not sequestered by plasma...$

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Machine Learning in Chemistry@ML_Chem · Jul 22

Unraveling fundamental activity–stability relationships in rutile oxides #machinelearning #compchem dx.doi.org/10.26434/chemr…

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Machine Learning in Chemistry@ML_Chem · Jul 22

Planar vs. Twisted Pyrimidine Derivatives: Insights from Molecular Dynamics and Predictive Modelling for Melamine Detection in Dairy Products #machinelearning #compchem pubs.rsc.org/en/Content/Art…

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Machine Learning in Chemistry@ML_Chem · Jul 22

Machine Learning Accelerated Screening Advanced Single-Atom Anchored MXenes Electrocatalyst for Nitrogen Fixation #machinelearning #compchem pubs.acs.org/doi/abs/10.102…

ML_Chem's tweet card. Electrochemical nitrogen reduction reaction (NRR) has garnered significant attention as an alternative to the energy-intensive Haber–Bosch process. However, the vast search space for electrocatalysts...

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