Research Articles
Active Learning for Solid-State Synthesis: AI-Driven Route Optimization in Materials Science and Drug Development
This article explores the transformative role of active learning (AL), a subfield of artificial intelligence, in optimizing solid-state synthesis routes—a critical challenge in materials science and drug development.
Real-Time Insights: How Machine Learning is Revolutionizing In-Line XRD Analysis in Biomedical Research
This article explores the transformative integration of machine learning (ML) for the in-line analysis of X-ray diffraction (XRD) patterns, a critical technique in materials science and drug development.
Tailor-Made Sensors in Synthesis Robots: Revolutionizing Automated Chemical Research and Drug Discovery
This article explores the transformative integration of custom-designed sensors into automated synthesis platforms, a key innovation accelerating research in chemistry and pharmaceuticals.
Data-Driven Discovery of Novel Inorganic Compounds: AI, Methods, and Real-World Applications in 2025
The discovery of novel inorganic compounds is critical for advancing technology in biomedicine, energy storage, and beyond.
Revolutionizing Materials Science: Autonomous Robotics and AI in Solid-State Synthesis of Inorganic Powders
This article explores the transformative integration of robotics, artificial intelligence, and automated laboratories in the solid-state synthesis of inorganic powders.
Deep Learning for Predicting Inorganic Material Synthesizability: Models, Applications, and Future Directions
The accurate prediction of inorganic material synthesizability is a critical challenge in accelerating the discovery of new functional materials for biomedical and technological applications.
Ab Initio Computations for Inorganic Synthesis: A Practical Guide to Target Screening and Materials Discovery
This article provides a comprehensive overview of the application of ab initio computations for screening and discovering inorganic materials.
Machine Learning for Solid-State Synthesis: From Data Challenges to Autonomous Recipe Generation
This article explores the transformative role of machine learning (ML) in overcoming the longstanding bottleneck of predictive solid-state synthesis.
Self-Driving Labs: How AI and Robotics Are Accelerating the Discovery of Novel Inorganic Materials
This article explores the transformative impact of autonomous laboratories on the discovery of novel inorganic materials.
Automated High-Throughput Synthesis of Inorganic Powders: Accelerating Discovery for Biomedicine and Beyond
This article explores the transformative impact of automated high-throughput synthesis on the development of inorganic powders and nanomaterials.