Research Articles
Beyond Trial-and-Error: AI, Automation, and High-Throughput Strategies for Accelerating Functional Thin Film Discovery
This article provides a comprehensive guide for researchers and development professionals on modern strategies to accelerate the discovery of functional thin films.
Semi-Supervised Learning for Material Synthesizability: A New Paradigm for Accelerated Discovery
Predicting material synthesizability remains a critical bottleneck in the discovery pipeline, compounded by the scarcity of labeled experimental data.
Synthesizability Model Training Data: A 2025 Guide to Preparation, Validation, and Application in Drug Discovery
This article provides a comprehensive guide for researchers and drug development professionals on preparing high-quality training data for synthesizability prediction models.
PU Learning for Synthesizability Classification: A Practical Guide for Drug Development and Materials Discovery
This comprehensive guide explores the implementation of Positive-Unlabeled (PU) learning to solve the critical challenge of synthesizability classification in drug development and materials science.
Beyond Charge Balancing: Predicting Synthesis Failure Rates in Advanced Inorganic Materials
This article explores the critical challenge of predicting the synthesizability of inorganic crystalline materials, a pivotal step in transitioning from computational design to real-world application.
Beyond Stability: How AI and Machine Learning Are Revolutionizing Synthesizability Prediction in Materials and Drug Discovery
The ability to accurately predict whether a theoretically designed material or drug molecule can be successfully synthesized is a critical bottleneck in discovery pipelines.
Beyond Simple Rules: Energy Above Hull vs. Charge Balancing for Predicting Material Synthesizability in Drug Development
This article provides a comprehensive analysis of two pivotal approaches for predicting material synthesizability: the thermodynamic metric of energy above hull and the heuristic rule of charge balancing.
Beyond Charge Balance: Why Modern Machine Learning is Redefining Material Synthesizability
For researchers and drug development professionals, accurately predicting which computationally designed materials can be synthesized is a critical bottleneck.
Machine Learning vs Heuristics for Material Synthesizability: A Data-Driven Guide for Researchers
Accelerating the discovery of novel functional materials and drug candidates is paramount, yet the practical challenge of synthesizability remains a major bottleneck.
Beyond Charge Balancing: Critical Limitations and Advanced Models for Accurate Synthesizability Prediction in Drug Development
This article critically examines the limitations of using charge balancing as a proxy for predicting material synthesizability, a crucial challenge in pharmaceutical development.