D3SC: Machine Learned Free Energies of Compounds

科罗拉多大学博尔德分校的查尔斯·马斯格雷夫和合作者亚伦·霍尔德得到了化学、生物工程、环境和运输系统部门的支持，以开发和应用机器学习方法来发现新材料。虽然元素周期表提供了许多可能的元素组合来形成材料，但这些化合物中只有一小部分是稳定的或具有特定应用所需的性质。此外，在大量可能的化合物中，只有大约1000种化合物具有已知的高温性质。在过去的五十年里，计算化学家们一直在使用量子力学的方程来发现新材料。然而，为特定的技术应用筛选大量候选材料仍然需要太多的计算，因此不现实。最近，统计学习方法已经发展起来，它可以从大量数据中提取系统信息，以训练高度可靠的人工智能模型来预测新系统的性能。在这个项目中，马斯格雷夫教授和霍尔德教授正在使用机器学习方法来预测材料的稳定性、结构和化学反应活性。然后，预测的性质可以用来确定催化具有重要技术意义的反应的候选材料，例如将水分解为氧气和氢气，将二氧化碳转化为有用的产品，或者从氮气和水中绿色合成氨。这些模型可以在公共储存库中以机器学习计算机代码的形式获得，也可以通过公众可访问的数据库获得。该项目正在培训高中生、本科生和研究生开发和应用最先进的机器学习方法，用于化学和化学工程应用。研究人员通过科罗拉多大学领导力与多样性(BOLD)中心参与了这一不断扩大的机会。将机器学习和化学中的新概念整合到课程中，并通过系LearnChemE YouTube平台。这个项目结合了电子结构、热力学、计算科学和机器学习的专业知识，研究分子的最基本属性之一--吉布斯自由能G(T)。这种数据驱动的方法利用的结果表明，在恒定体积准谐近似下计算的振动熵和亥姆霍兹自由能-对G(T)至关重要，但对量子力学计算具有挑战性-具有系统的温度依赖性，可以使用机器学习结合材料化学成分的知识进行准确和高效的预测。研究人员正在将这一观察扩展到将机器学习方法直接应用于G(T)模型，使用数百个分子的实验数据进行训练和描述符提取。由此得到的描述符正用于预测无机晶体结构数据库中列出的约20,000种独特成分的热化学数据，并反过来计算与温度相关的凸包相图和固态反应平衡。模型和G(T)数据在大型数据库中可用。新的方法能够发现一般趋势和关于温度和组成对反应性、可合成性、稳定性和亚稳性的影响的新的化学知识。除了对分子和反应的热化学提供深入的见解外，这项研究还使识别异常成为可能，这些异常可能表明新出现的性质正在改变分子的行为。例如，依赖于温度的涌现或量子现象创造了独特的材料属性。尽管先进材料在广泛的技术中具有技术和经济上的重要性，但导致其稳定性和反应性的详细行为仍有许多未知之处。产生的新技术和热化学数据库的潜在应用包括使用氧化还原材料的热化学水分解，通过化学循环合成氨，氧化化学，氧化物的碳热还原，以及分子氢或其他还原剂的分子还原。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Charles Musgrave of the University of Colorado Boulder and collaborator Aaron Holder are supported by the Division of Chemistry, and the Division of Chemical, Bioengineering, Environmental, and Transport Systems, to develop and apply machine learning approaches for the discovery of new materials. While the periodic table provides a many possible combinations of elements from which to form materials, only a fraction of these compounds will be stable or have desirable properties for particular applications. Furthermore, of the large number of possible compounds, only about one thousand have known properties at elevated temperatures. For the past fifty years, computational chemists have used equations of quantum mechanics to discover new materials. However, screening large numbers of candidate materials for a specific technological application remains too computationally demanding to be practical. Recently, statistical learning approaches have been developed which can extract systematic information from large quantities of data to train highly reliable "artificial intelligence" models for predicting properties of a new system. In this project, Professors Musgrave and Holder are using machine learning approaches applied to predict the stabilities, structures, and chemical reactivity of materials. The predicted properties can then be used to identify candidate materials for catalyzing technologically-important reactions, such as splitting water into oxygen and hydrogen, converting carbon dioxide into useful products, or the 'green' synthesis of ammonia from nitrogen and water. The models are available on public repositories as machine learning computer codes, and through publicly-accessible databases. The project is training high school, undergraduate and graduate students in the development and application of state-of-the-art machine learning methods for chemistry and chemical engineering applications. The researchers participation in the Broadening Opportunity through the Leadership and Diversity (BOLD) Center at University of Colorado. The incorporation of new concepts in machine learning and chemistry are integrated into courses and through the departmental LearnChemE YouTube platform.This project combines expertise in electronic structure, thermodynamics, computational science, and machine learning to study one of the most fundamental properties of molecules--the Gibbs free energy, G(T). The data-driven approach takes advantage of results showing that the vibrational entropy and Helmholtz free energy computed in the constant-volume quasiharmonic approximation - quantities that critically contribute to G(T) but are computationally challenging to calculate quantum-mechanically - have systematic temperature dependence and can be accurately and efficiently predicted using machine learning, coupled with knowledge of the chemical composition of the material. The researchers are extending this observation to apply machine learning methods to model G(T) directly, using experimental data for several hundred molecules for training and descriptor extraction. The resulting descriptors are being used to predict thermochemical data for ~20,000 unique compositions tabulated in the Inorganic Crystal Structure Database, and in turn, to compute temperature-dependent convex hull phase diagrams and solid-state reaction equilibria. The models and G(T) data are available on large databases. The new methodology is enabling the discovery of general trends and new chemical knowledge of the effects of temperature and composition on reactivity, synthesizability, stability and metastability. In addition to providing deep insights into the thermochemistry of molecules and reactions, this research is enabling the identification of anomalies that may indicate systems where emerging properties are altering the behavior of the molecule. For example, where temperature-dependent emergent or quantum phenomena create unique materials properties. Despite the technological and economic importance of advanced materials in a broad range of technologies, much is still unknown about the detailed behavior that give rise to their stability and reactivity. Potential applications of the new techniques and thermochemical databases produced include thermochemical water splitting using redox materials, ammonia synthesis by chemical looping, oxidation chemistries, carbothermal reduction of oxides, and reduction of molecules by molecular hydrogen or other reductants.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.