Machine learning methods for small data challenges in molecular science
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
Exploring the Structural, Dynamic, and Functional Properties of Metal‐Organic Frameworks through Molecular Modeling
This review spotlights the role of atomic‐level modeling in research on metal‐organic
frameworks (MOFs), especially the key methodologies of density functional theory (DFT) …
frameworks (MOFs), especially the key methodologies of density functional theory (DFT) …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model
Transition state search is key in chemistry for elucidating reaction mechanisms and
exploring reaction networks. The search for accurate 3D transition state structures, however …
exploring reaction networks. The search for accurate 3D transition state structures, however …
Modeling Interfacial Dynamics on Single Atom Electrocatalysts: Explicit Solvation and Potential Dependence
Conspectus Single atom electrocatalysts, with noble metal-free composition, maximal atom
efficiency, and exceptional reactivity toward various energy and environmental applications …
efficiency, and exceptional reactivity toward various energy and environmental applications …
Informative Training Data for Efficient Property Prediction in Metal–Organic Frameworks by Active Learning
In recent data-driven approaches to material discovery, scenarios where target quantities
are expensive to compute and measure are often overlooked. In such cases, it becomes …
are expensive to compute and measure are often overlooked. In such cases, it becomes …
DELFI: a computer oracle for recommending density functionals for excited states calculations
Density functional theory (DFT) is the workhorse of computational quantum chemistry. One
of its main limitations is that choosing the right functional is a non-trivial task left for human …
of its main limitations is that choosing the right functional is a non-trivial task left for human …
Identifying and embedding transferability in data-driven representations of chemical space
Transferability, especially in the context of model generalization, is a paradigm of all
scientific disciplines. However, the rapid advancement of machine learned model …
scientific disciplines. However, the rapid advancement of machine learned model …
Combining molecular quantum mechanical modeling and machine learning for accelerated reaction screening and discovery
Molecular quantum mechanical modeling, accelerated by machine learning, has opened the
door to high‐throughput screening campaigns of complex properties, such as the activation …
door to high‐throughput screening campaigns of complex properties, such as the activation …
Improving the reliability of, and confidence in, DFT functional benchmarking through active learning
Validating the performance of exchange-correlation functionals is vital to ensure the
reliability of density functional theory (DFT) calculations. Typically, these validations involve …
reliability of density functional theory (DFT) calculations. Typically, these validations involve …