An ecosystem for digital reticular chemistry
The vastness of the materials design space makes it impractical to explore using traditional
brute-force methods, particularly in reticular chemistry. However, machine learning has …
brute-force methods, particularly in reticular chemistry. However, machine learning has …
Data-driven strategies for accelerated materials design
R Pollice, G dos Passos Gomes… - Accounts of Chemical …, 2021 - ACS Publications
Conspectus The ongoing revolution of the natural sciences by the advent of machine
learning and artificial intelligence sparked significant interest in the material science …
learning and artificial intelligence sparked significant interest in the material science …
The role of machine learning in the understanding and design of materials
Developing algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …
enable us to systematically find novel materials, which can have huge technological and …
Autonomous molecular design: then and now
T Dimitrov, C Kreisbeck, JS Becker… - … applied materials & …, 2019 - ACS Publications
The success of deep machine learning in processing of large amounts of data, for example,
in image or voice recognition and generation, raises the possibilities that these tools can …
in image or voice recognition and generation, raises the possibilities that these tools can …
Into the unknown: how computation can help explore uncharted material space
Novel functional materials are urgently needed to help combat the major global challenges
facing humanity, such as climate change and resource scarcity. Yet, the traditional …
facing humanity, such as climate change and resource scarcity. Yet, the traditional …
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 …
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 …
Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
A GPT‐4 Reticular Chemist for Guiding MOF Discovery
We present a new framework integrating the AI model GPT‐4 into the iterative process of
reticular chemistry experimentation, leveraging a cooperative workflow of interaction …
reticular chemistry experimentation, leveraging a cooperative workflow of interaction …