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Machine-learning-assisted materials discovery using failed experiments

Machine-learning-assisted materials discovery using failed experiments (caption)

Failed experiments are not useless. They even have a lot of value! Raccuglia et al. have published an article titled "Machine-learning-assisted materials discovery using failed experiments" (Nature 2016, 533, 73-76), in which they present how they exploited failed or unsuccessful attempts at synthesizing vanadium selenites to train a machine learning program to predict the outcomes of the syntheses of other vanadium selenites with never-tested organic building blocks. The authors also studied how the machine learning model made its predictions and revealed new hypotheses about the requirements for successful synthesis of templated vanadium selenites. Their methodology is summarized in the figure below.

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Using Machine Learning To Predict Suitable Conditions for Organic Reactions

Using Machine Learning To Predict Suitable Conditions for Organic Reactions (caption)

Finding the right experimental conditions (solvent, temperature, catalyst, additives, etc.) for a reaction can be a very time-consuming process. Gao et al. have published an article titled "Using Machine Learning To Predict Suitable Conditions for Organic Reactions" (ACS Cent. Sci. 2018, 4, 1465-1476), in which they present a neural network model that is able to predict appropriate reaction conditions for any organic reaction, including a catalyst, solvents, reagents and the temperature.

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A graph-convolutional neural network model for the prediction of chemical reactivity

A graph-convolutional neural network model for the prediction of chemical reactivity (caption)

To synthesize a molecule, a chemist has to imagine a sequence of possible chemical transformations that could produce it, based on his/her knowledge and the scientific literature, and then perform the reactions in a laboratory, hoping that they happen as expected and give the desired product. Any chemist who has spent some time in a laboratory attempting to synthesize molecules knows that chemical reactions often behave in unwanted ways:

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Deep-learning-based inverse design model for intelligent discovery of organic molecules

Deep-learning-based inverse design model for intelligent discovery of organic molecules (caption)

Discovering new organic molecules that possess a given property is not an easy task. It is a process that is iterative, experiment-intensive and tedious. Furthermore, designing large numbers (let's say hundreds or thousands of them) of new molecules for an application is extremely challenging, even for the most creative chemists. In our last blog post, we presented an AI-based tool that is able to automatically design new molecules, based on a continuous representation of molecules.

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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules (caption)

Designing molecules that have desired properties is a long and difficult process. To obtain a new molecule that can be used in some application, scientists must use their creativity and domain knowledge to propose many new molecules, synthesize them and test them for the given application. Moreover, human creativity often has its limits in the number and diversity of ideas that it can generate.

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Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments (caption)

Metallic glasses are amorphous alloys of metals and metalloids. They usually have properties that are very different from crystalline alloys: exceptional mechanical performances (eg. yield strength and wear resistance) and sometimes improved corrosion resistance or high magnetic permeability.

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Optimizing Chemical Reactions with Deep Reinforcement Learning

Optimizing Chemical Reactions with Deep Reinforcement Learning (caption)

Optimizing chemical reactions is a very common task for chemists. It usually aims at maximizing the yield or selectivity of a reaction in order to get the most possible product from some raw material.

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Launching ChemIntelligence

Launching ChemIntelligence (caption)

Artificial intelligence (AI) will change the way we make chemistry and materials and make it faster, by better targeting experiments that we run in the laboratory. This will allow to increase the return on investment of chemistry and materials R&D.

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