The Deep Learning for Multi-phase Organic Chemistry Conference; Irvine, California; September 27-28, 2018

本次研讨会汇集了来自不同学科的专家，包括计算机科学家、计算化学家和大气化学家，讨论可能导致能够准确预测非理想多相系统中复杂环境化学的人工有机化学家发展的协议。研讨会的目标是确定利用计算方法来解决复杂化学问题的策略。研讨会的参与者包括20-25名研究人员，代表多相化学和计算的横截面，以确定多相系统计算化学中的关键开放问题，并制定解决这些问题的方案。将包括的主题包括讨论涉及理论、数值模拟（例如统计力学和密度泛函理论的建模）以及实验室和现场实验的最佳组合，以测试深度学习技术。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This workshop is bringing together experts from different disciplines, including computer scientists, computational chemists and atmospheric chemists to discuss protocols that may lead to development of an artificial organic chemist capable of accurately predicting complex environmental chemistry in non-ideal multiphase systems. The goal of the workshop is to identify strategies that capitalize on computational approaches to solve complex chemistry. The participants of the workshop include 20-25 researchers, representing a cross-section across multiphase chemistry and computation, to identify critical open questions in computational chemistry for multiphase systems, as well as to formulate protocols for answering those questions. Topics to be included include discussions on the best combination of experiments involving theory, numerical simulations (e.g. modeling of statistical mechanics and density functional theory), and laboratory and field experimentation to test deep learning techniques.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.