Inverse molecular design using machine learning: Generative models for matter engineering
B Sanchez-Lengeling, A Aspuru-Guzik - Science, 2018 - science.org
The discovery of new materials can bring enormous societal and technological progress. In
this context, exploring completely the large space of potential materials is computationally …
this context, exploring completely the large space of potential materials is computationally …
Generative models for automatic chemical design
D Schwalbe-Koda, R Gómez-Bombarelli - Machine Learning Meets …, 2020 - Springer
Materials discovery is decisive for tackling urgent challenges related to energy, the
environment, health care, and many others. In chemistry, conventional methodologies for …
environment, health care, and many others. In chemistry, conventional methodologies for …
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 …
Modern machine learning for tackling inverse problems in chemistry: molecular design to realization
The discovery of new molecules and materials helps expand the horizons of novel and
innovative real-life applications. In pursuit of finding molecules with desired properties …
innovative real-life applications. In pursuit of finding molecules with desired properties …
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 …
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 …
Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials
A major challenge in materials design is how to efficiently search the vast chemical design
space to find the materials with desired properties. One effective strategy is to develop …
space to find the materials with desired properties. One effective strategy is to develop …
Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES
Inverse design allows the generation of molecules with desirable physical quantities using
property optimization. Deep generative models have recently been applied to tackle inverse …
property optimization. Deep generative models have recently been applied to tackle inverse …
Data-driven algorithms for inverse design of polymers
The ever-increasing demand for novel polymers with superior properties requires a deeper
understanding and exploration of the chemical space. Recently, data-driven approaches to …
understanding and exploration of the chemical space. Recently, data-driven approaches to …
Deep generative models for materials discovery and machine learning-accelerated innovation
AS Fuhr, BG Sumpter - Frontiers in Materials, 2022 - frontiersin.org
Machine learning and artificial intelligence (AI/ML) methods are beginning to have
significant impact in chemistry and condensed matter physics. For example, deep learning …
significant impact in chemistry and condensed matter physics. For example, deep learning …