Scholarlink

<p>Data-driven strategies are reshaping computational materials design by accelerating the prediction of compounds with targeted functionalities. Beyond high-throughput screening, the integration of generative artificial intelligence enables exploration across vast chemical spaces comprising millions of known and hypothetical materials. This abundance of candidates presents a challenge: identifying which candidate compounds are not only low in energy but also synthetically accessible. Here we assess advances towards closing this synthesis gap for inorganic crystals. These include the incorporation of thermodynamic potentials (from internal energies at 0 K to Gibbs free energies at reaction conditions)—crucial for evaluating phase stability and reaction driving forces—chemical heuristics (from charge neutrality to electronegativity rules) and machine learning models (from positive unlabelled learning to large language models) to guide compound selection and prioritization. Looking forward, the development of more robust synthesizability metrics, synthesis planning tools, and agentic workflows integrating experimental feedback will narrow the divide between virtual screening and real-world materials realization.</p><p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Closing the synthesis gap in computational materials design

  • 期刊论文

  • 发表日期：

    2026年5月6日

查看全文
  • Hyunsoo Park,
  • Kinga O. Mastej,
  • Panyalak Detrattanawichai,
  • Ryan Nduma,
  • Aron Walsh

摘要

Data-driven strategies are reshaping computational materials design by accelerating the prediction of compounds with targeted functionalities. Beyond high-throughput screening, the integration of generative artificial intelligence enables exploration across vast chemical spaces comprising millions of known and hypothetical materials. This abundance of candidates presents a challenge: identifying which candidate compounds are not only low in energy but also synthetically accessible. Here we assess advances towards closing this synthesis gap for inorganic crystals. These include the incorporation of thermodynamic potentials (from internal energies at 0 K to Gibbs free energies at reaction conditions)—crucial for evaluating phase stability and reaction driving forces—chemical heuristics (from charge neutrality to electronegativity rules) and machine learning models (from positive unlabelled learning to large language models) to guide compound selection and prioritization. Looking forward, the development of more robust synthesizability metrics, synthesis planning tools, and agentic workflows integrating experimental feedback will narrow the divide between virtual screening and real-world materials realization.