Machine-enabled inverse design of inorganic solid materials: promises and challenges
Developing high-performance advanced materials requires a deeper insight and search into
the chemical space. Until recently, exploration of materials space using chemical intuitions …
the chemical space. Until recently, exploration of materials space using chemical intuitions …
Chemical data intelligence for sustainable chemistry
This study highlights new opportunities for optimal reaction route selection from large
chemical databases brought about by the rapid digitalisation of chemical data. The chemical …
chemical databases brought about by the rapid digitalisation of chemical data. The chemical …
Neural recommender system for the activity coefficient prediction and UNIFAC model extension of ionic liquid‐solute systems
For the ionic liquid (IL)‐solute systems of broad interest, a deep neural network based
recommender system (RS) for predicting the infinite dilution activity coefficient (γ∞) is …
recommender system (RS) for predicting the infinite dilution activity coefficient (γ∞) is …
[HTML][HTML] Guest editorial: Special topic on data-enabled theoretical chemistry
Welcome to the Journal of Chemical Physics Special Topic on data-enabled theoretical
chemistry. We expect that this will be a timely addition to this new and rapidly evolving field …
chemistry. We expect that this will be a timely addition to this new and rapidly evolving field …
Drawing a materials map with an autoencoder for lithium ionic conductors
Y Yamaguchi, T Atsumi, K Kanamori, N Tanibata… - Scientific Reports, 2023 - nature.com
Efforts to optimize known materials and enhance their performance are ongoing, driven by
the advancements resulting from the discovery of novel functional materials. Traditionally …
the advancements resulting from the discovery of novel functional materials. Traditionally …
Multifidelity information fusion with machine learning: A case study of dopant formation energies in hafnia
Cost versus accuracy trade-offs are frequently encountered in materials science and
engineering, where a particular property of interest can be measured/computed at different …
engineering, where a particular property of interest can be measured/computed at different …
Prediction of infinite‐dilution activity coefficients with neural collaborative filtering
Accurate prediction of infinite dilution activity coefficient (γ∞) for phase equilibria and
process design is crucial. In this work, an experimental γ∞ dataset containing 295 solutes …
process design is crucial. In this work, an experimental γ∞ dataset containing 295 solutes …
Data-driven discovery of photoactive quaternary oxides using first-principles machine learning
We present a low-cost, virtual high-throughput materials design workflow and use it to
identify earth-abundant materials for solar energy applications from the quaternary oxide …
identify earth-abundant materials for solar energy applications from the quaternary oxide …
Element similarity in high-dimensional materials representations
The traditional display of elements in the periodic table is convenient for the study of
chemistry and physics. However, the atomic number alone is insufficient for training …
chemistry and physics. However, the atomic number alone is insufficient for training …
Data-centric science for materials innovation
I Tanaka, K Rajan, C Wolverton - MRS Bulletin, 2018 - cambridge.org
With the development of high-speed computers, networks, and huge storage, researchers
can utilize a large volume and wide variety of materials data generated by experimental …
can utilize a large volume and wide variety of materials data generated by experimental …