An autonomous laboratory for the accelerated synthesis of novel materials (2023-11-29T00:00:00.000000Z)

TL;DR

The A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders, is presented that combines computations, literature data, machine learning and active learning, which discovered and synthesized 41 novel compounds from a set of 58 targets after 17 days of operation.

Abstract

An autonomous laboratory, the A-Lab, is presented that combines computations, literature data, machine learning and active learning, which discovered and synthesized 41 novel compounds from a set of 58 targets after 17 days of operation. To close the gap between the rates of computational screening and experimental realization of novel materials^ 1 , 2 , we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

Authors

References48 items

Autonomous and dynamic precursor selection for solid-state materials synthesis

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

An autonomous portable platform for universal chemical synthesis

Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions

Review of computational approaches to predict the thermodynamic stability of inorganic solids

Role of Pairwise Reactions on the Synthesis of Li0.3La0.57TiO3 and the Resulting Structure-Property Correlations.

Raman imaging of ferroelastically configurable Jahn–Teller domains in LaMnO3

Selectivity in Yttrium Manganese Oxide Synthesis via Local Chemical Potentials in Hyperdimensional Phase Space.

A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra

Recent Developments on Molten Salt Synthesis of Inorganic Nanomaterials: A Review

A data fusion approach to optimize compositional stability of halide perovskites

Rational Solid-State Synthesis Routes for Inorganic Materials.

Observing and Modeling the Sequential Pairwise Reactions that Drive Solid‐State Ceramic Synthesis

A mobile robotic chemist

Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific Literature

Efficient Closed-loop Maximization of Carbon Nanotube Growth Rate using Bayesian Optimization

The interplay between thermodynamics and kinetics in the solid-state synthesis of layered oxides

Synthesis of Metastable Inorganic Solids with Extended Structures.

Selective metathesis synthesis of MgCr2S4 by control of thermodynamic driving forces

Text-mined dataset of inorganic materials synthesis recipes

A robotic platform for flow synthesis of organic compounds informed by AI planning

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods

Self-driving laboratory for accelerated discovery of thin-film materials

The glass-forming ability explained from local structural differences by NMR between glasses and crystals in alkali metaphosphates

A scripting interface for GSAS-II

Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry

Thermodynamic limit for synthesis of metastable inorganic materials

Digitization of multistep organic synthesis in reactionware for on-demand pharmaceuticals

High-temperature behaviour of ammonium dihydrogen phosphate

Proximal Policy Optimization Algorithms

Toward Reaction-by-Design: Achieving Kinetic Control of Solid State Chemistry with Metathesis

Autonomy in materials research: a case study in carbon nanotube growth

Accurate first-principles structures and energies of diversely bonded systems from an efficient density functional.

OpenAI Gym

Interface Stability in Solid-State Batteries

Computational predictions of energy materials using density functional theory

Synthesis of many different types of organic small molecules using one automated process

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

GSAS‐II: the genesis of a modern open‐source all purpose crystallography software package

Handbook of inorganic substances

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

The crystal structure of lanthanum manganate(iii), LaMnO3, at room temperature and at 1273 K under N2

The elastic constants and structure of the vitreous system Mo-P-O

Inorganic synthesis recommendation by machine learning materials similarity from scientific literature

Non-stoichiometry, structural defects and properties of LaMnO3+δ with high δ values (0.11≤δ≤0.29)

Nature | Vol 624 | 7 December 2023 | 91 high δ values (0.11≤ δ ≤0.29)