Generative models as an emerging paradigm in the chemical sciences
DM Anstine, O Isayev - Journal of the American Chemical Society, 2023 - ACS Publications
Traditional computational approaches to design chemical species are limited by the need to
compute properties for a vast number of candidates, eg, by discriminative modeling …
compute properties for a vast number of candidates, eg, by discriminative modeling …
In pursuit of the exceptional: Research directions for machine learning in chemical and materials science
Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …
technologically valuable and fundamentally interesting, because they often involve new …
Data-driven pursuit of electrochemically stable 2D materials with basal plane activity toward oxygen electrocatalysis
In silico design of efficient electrocatalysts for the oxygen reduction/evolution reaction
(ORR/OER) is vital for developing the hydrogen economy. However, practical design …
(ORR/OER) is vital for developing the hydrogen economy. However, practical design …
Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials
GJO Beran - Chemical Science, 2023 - pubs.rsc.org
The reliability of organic molecular crystal structure prediction has improved tremendously in
recent years. Crystal structure predictions for small, mostly rigid molecules are quickly …
recent years. Crystal structure predictions for small, mostly rigid molecules are quickly …
Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory
The past decade has witnessed the significant efforts in novel material discovery in the use
of data-driven techniques, in particular, machine learning (ML). However, since it needs to …
of data-driven techniques, in particular, machine learning (ML). However, since it needs to …
Predictive Synthesis of Copper Selenides Using a Multidimensional Phase Map Constructed with a Data-Driven Classifier
Copper selenides are an important family of materials with applications in catalysis,
plasmonics, photovoltaics, and thermoelectrics. Despite being a binary material system, the …
plasmonics, photovoltaics, and thermoelectrics. Despite being a binary material system, the …
[HTML][HTML] Advances in hydrogen storage materials: harnessing innovative technology, from machine learning to computational chemistry, for energy storage solutions
The demand for clean and sustainable energy solutions is escalating as the global
population grows and economies develop. Fossil fuels, which currently dominate the energy …
population grows and economies develop. Fossil fuels, which currently dominate the energy …
Identifying underexplored and untapped regions in the chemical space of transition metal complexes
We survey more than 240 000 crystallized mononuclear transition metal complexes (TMCs)
to identify trends in preferred geometric structure and metal coordination. While we observe …
to identify trends in preferred geometric structure and metal coordination. While we observe …
Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review
In the last few decades, the influence of machine learning has permeated many areas of
science and technology, including the field of materials science. This toolkit of data driven …
science and technology, including the field of materials science. This toolkit of data driven …
[HTML][HTML] Artificial intelligence generates novel 3D printing formulations
Abstract Formulation development is a critical step in the development of medicines. The
process requires human creativity, ingenuity and in-depth knowledge of formulation …
process requires human creativity, ingenuity and in-depth knowledge of formulation …