Machine-learned interatomic potentials: Recent developments and prospective applications

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Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...

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Machine-learned interatomic potentials: Recent developments and prospective applications

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Efficient and insightful descriptors for representing molecular and material space

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Predicting protein interactions using geometric deep learning on protein surfaces

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Computational drug development for membrane protein targets

From Sequence to Dynamics to Function: Computational Design of Allostery and Ligand Selectivity in G-Protein Coupled Receptors

Oxidative Defect Detection Within Free and Packed DNA Systems: A Quantum Mechanical/Molecular Mechanics (QM/ MM) Approach

Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy

Investigating the intra-molecular and inter-molecular effects of post-translational modifications on intrinsically disordered protein regions and structured protein regions

Machine learning-aided generative molecular design

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Prolamins' 3D structure: A new insight into protein modeling using the language of numbers and shapes

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Live-cell imaging powered by computation

Universal machine learning for the response of atomistic systems to external fields

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

A new age in protein design empowered by deep learning

A Geometric Transformer for Structural Biology: Development and Applications of the Protein Structure Transformer

Chemical Shift-Dependent Interaction Maps in Molecular Solids