Building the MIT Schwarzman College of Computing

Broad multidisciplinary computing is transforming our modern life in many aspects,and computer science itself is also broadening.In response to these trends,the Massachusetts Institute of Technology(MIT)recently built a new college,the Schwarzman College of Computing.The missions of the College are to support rapid growth of computing fields,facilitate computing collaborations across departments and disciplines,and to focus on social,ethical,and policy issues in computing.This paper begins by introducing the history and design of the Schwarzman College.Then,it discusses the new opportunities that the College of computing has created at MIT,specifically the common ground for computing education and the social and ethical responsibilities of computing programs.

Panel Discussion on“ The Role of Computer Science in University Development Strategy”

At the end of the keynotes during the second Global Forum of Development of Computer Science,a panel discussion was held to encourage further discussion on ways for universities to adapt to the rapidly changing computer science field.Five deans of top computer science departments participated,including the moderator.The discussions were guided along three topics,namely the role of computer science departments in universities today,the nature of computer science as a fundamental discipline or an applied one,and computer science education.Out of these topics,the panelists mainly focused on the interdisciplinary nature of computer science in teaching,research,and industry.The panelists agreed about ways to prepare for the interdisciplinary future,for example by establishing new research centers,introducing projectbased curricula,and collaborating with industry while keeping the campus vibrant.They also pointed out that universities may be under equipped for preparing future professionals to keep up with rapid new advances,especially in machine learning and artificial intelligence.

Designing architected materials for mechanical compression via simulation, deep learning, and experimentation

Architected materials can achieve enhanced properties compared to their plain counterparts.Specific architecting serves as a powerful design lever to achieve targeted behavior without changing the base material.Thus,the connection between architected structure and resultant properties remains an open field of great interest to many fields,from aerospace to civil to automotive applications.Here,we focus on properties related to mechanical compression,and design hierarchical honeycomb structures to meet specific values of stiffness and compressive stress.To do so,we employ a combination of techniques in a singular workflow,starting with molecular dynamics simulation of the forward design problem,augmenting with data-driven artificial intelligence models to address the inverse design problem,and verifying the behavior of de novo structures with experimentation of additively manufactured samples.We thereby demonstrate an approach for architected design that is generalizable to multiple material properties and agnostic to the identity of the base material.

Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks

Structural defects are abundant in solids,and vital to the macroscopic materials properties.However,a defect-property linkage typically requires significant efforts from experiments or simulations,and often contains limited information due to the breadth of nanoscopic design space.Here we report a graph neural network(GNN)-based approach to achieve direct translation between mesoscale crystalline structures and atom-level properties,emphasizing the effects of structural defects.Our end-to-end method offers great performance and generality in predicting both atomic stress and potential energy of multiple systems with different defects.Furthermore,the approach also precisely captures derivative properties which strictly observe physical laws and reproduces evolution of properties with varying boundary conditions.By incorporating a genetic algorithm,we then design de novo atomic structures with optimum global properties and target local patterns.The method would significantly enhance the efficiency of evaluating atomic behaviors given structural imperfections and accelerates the design process at the meso-level.