DMREF: Collaborative Research: The Synthesis Genome: Data Mining for Synthesis of New Materials

DMREF：协作研究：合成基因组：新材料合成的数据挖掘

• 批准号: 1534431
• 负责人: Andrew McCallum
• 金额: $ 36.39万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1534431&HistoricalAwards=false
• 关键词: DMREF; Collaborative; Research; Synthesis; Genome

NON-TECHNICAL:Development of new materials is the key to addressing many of the technical challenges our society faces from energy storage to water treatment and purification. To offer just a few examples: in the oil industry, new materials are needed to withstand aggressive conditions, where failure comes with tremendous cost; electrified vehicle drive trains will be advanced by higher performing battery electrodes; carbon dioxide capture requires inexpensive new materials with the proper thermodynamic and kinetic behavior towards absorption and release. The rapid design of novel materials has been transformed by approaches where properties for many tens of thousands of materials can be predicted or inferred by a computer. The pace of commercially-realized advanced materials seems now to be limited by trial-and-error synthesis techniques. In other words, researchers have accelerated the process of knowing what to make such that the bottleneck is now how to make the structures. This research will learn from existing knowledge to develop insight on the synthesis of inorganic compounds. The analytical foundation of these activities stems from advances in machine learning that has allowed computers to excel in typically "human" tasks such as health care diagnoses and game show participation. This research will further accelerate the goals of efforts such as the Materials Genome Initiative for Global Competitiveness by enabling efficient synthesis of novel materials thereby speeding up evaluation of newly suggested materials.TECHNICAL:Materials are a key bottleneck in many technological advances such as efficient catalysis, clean energy generation, and water filtration. Materials Genome Initiative-style efforts have produced several examples of computationally designed materials in the fields of energy storage, catalysis, thermoelectrics, and hydrogen storage, as well as large data resources that can be used to screen for potentially transformative compounds. These successes in accelerated materials design have moved the bottleneck in materials development towards the synthesis of novel compounds, and much of the momentum and efficiency gained in the design process becomes gated by trial-and-error synthesis techniques. This research will do for solid state advanced materials synthesis what modern computational methods are doing for materials properties: Build predictive tools for synthesis so that targeted compounds can be synthesized more rapidly. This work will combine knowledge regarding synthesis, first principles modeling, and data mining to suggest synthesis routes for novel compounds.

非技术：新材料的开发是解决我们社会面临的许多技术挑战的关键，从能源储存到水处理和净化。举几个例子：在石油行业，需要新材料来承受恶劣的条件，在这种条件下，失败会带来巨大的成本；更高性能的电池电极将推动电动汽车传动系统的发展；二氧化碳捕获需要廉价的新材料，具有适当的吸收和释放热力学和动力学行为。通过计算机可以预测或推断成千上万种材料的特性，这种方法已经改变了新材料的快速设计。商业上实现先进材料的步伐现在似乎受到试错合成技术的限制。换句话说，研究人员已经加快了了解制造什么的过程，现在的瓶颈是如何制造这些结构。这项研究将从现有的知识中学习，以发展对无机化合物合成的见解。这些活动的分析基础源于机器学习的进步，这使得计算机在医疗诊断和游戏节目参与等典型的“人类”任务中表现出色。这项研究将进一步加速诸如材料基因组计划的目标，使新材料的有效合成成为可能，从而加快对新材料的评估。技术：材料是许多技术进步的关键瓶颈，如高效催化、清洁能源发电和水过滤。材料基因组计划式的努力已经在能源储存、催化、热电和储氢领域产生了几个计算设计材料的例子，以及可用于筛选潜在变革性化合物的大型数据资源。这些加速材料设计的成功已经将材料开发的瓶颈移向了新化合物的合成，并且在设计过程中获得的大部分动力和效率都被试错合成技术所限制。这项研究将为固态先进材料合成提供现代计算方法为材料特性所做的工作：建立合成预测工具，以便更快地合成目标化合物。这项工作将结合有关合成、第一性原理建模和数据挖掘的知识来建议新化合物的合成路线。