Abstract
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 intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials.
Keywords
Generative grammarContext (archaeology)Computer scienceVariety (cybernetics)PaceArtificial intelligenceGenerative DesignBiochemical engineeringMachine learningEngineeringBiology
Affiliated Institutions
Related Publications
Generative adversarial networks
Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a c...
LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data
Linked data consist of both node attributes, e.g., Preferences, posts and degrees, and links which describe the connections between nodes. They have been widely used to represen...
A ConvNet for the 2020s
The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification...
A general approach for developing system‐specific functions to score protein–ligand docked complexes using support vector inductive logic programming
Abstract Despite the increased recent use of protein–ligand and protein–protein docking in the drug discovery process due to the increases in computational power, the difficulty...
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. T...
Publication Info
- Year
- 2018
- Type
- review
- Volume
- 361
- Issue
- 6400
- Pages
- 360-365
- Citations
- 1843
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
1843
OpenAlex
Cite This
Benjamín Sánchez-Lengeling,
Alán Aspuru‐Guzik
(2018).
Inverse molecular design using machine learning: Generative models for matter engineering.
Science
, 361
(6400)
, 360-365.
https://doi.org/10.1126/science.aat2663
Identifiers
- DOI
- 10.1126/science.aat2663