Quantum chemistry-informed active learning to accelerate the design and discovery of sustainable energy storage materials
We employed density functional theory (DFT) to compute oxidation potentials of 1400
homobenzylic ether molecules to search for the ideal sustainable redoxmer design. The …
homobenzylic ether molecules to search for the ideal sustainable redoxmer design. The …
Discovery of energy storage molecular materials using quantum chemistry-guided multiobjective bayesian optimization
Redox flow batteries (RFBs) are a promising technology for stationary energy storage
applications due to their flexible design, scalability, and low cost. In RFBs, energy is carried …
applications due to their flexible design, scalability, and low cost. In RFBs, energy is carried …
[HTML][HTML] Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries
Advances in the field of goal-directed molecular optimization offer the promise of finding
feasible candidates for even the most challenging molecular design applications. One …
feasible candidates for even the most challenging molecular design applications. One …
Discovery of lead low-potential radical candidates for organic radical polymer batteries with machine-learning-assisted virtual screening
The discovery and development of new low reduction potential molecules that also have fast
charge transfer kinetics is necessary for the further development of organic redox-active …
charge transfer kinetics is necessary for the further development of organic redox-active …
[HTML][HTML] Comparison of computational chemistry methods for the discovery of quinone-based electroactive compounds for energy storage
High-throughput computational screening (HTCS) is a powerful approach for the rational
and time-efficient design of electroactive compounds. The effectiveness of HTCS is …
and time-efficient design of electroactive compounds. The effectiveness of HTCS is …
Machine learning for renewable energy materials
Achieving the 2016 Paris agreement goal of limiting global warming below 2° C and
securing a sustainable energy future require materials innovations in renewable energy …
securing a sustainable energy future require materials innovations in renewable energy …
[HTML][HTML] Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization
Discovering novel chemicals and materials can be greatly accelerated by iterative machine
learning-informed proposal of candidates—active learning. However, standard global error …
learning-informed proposal of candidates—active learning. However, standard global error …
Human-and machine-centred designs of molecules and materials for sustainability and decarbonization
Breakthroughs in molecular and materials discovery require meaningful outliers to be
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …
Hunting for organic molecules with artificial intelligence: molecules optimized for desired excitation energies
This work presents a proof-of-concept study in artificial-intelligence-assisted (AI-assisted)
chemistry where a machine-learning-based molecule generator is coupled with density …
chemistry where a machine-learning-based molecule generator is coupled with density …
[HTML][HTML] Molecular generation targeting desired electronic properties via deep generative models
Q Yuan, A Santana-Bonilla, MA Zwijnenburg, KE Jelfs - Nanoscale, 2020 - pubs.rsc.org
As we seek to discover new functional materials, we need ways to explore the vast chemical
space of precursor building blocks, not only generating large numbers of possible building …
space of precursor building blocks, not only generating large numbers of possible building …