Molecular Dynamics: A Study on Slip, Drops, and Graphene

Molecular dynamics (MD) is a computational tool used to study physical systems by modeling the atomic-scale interactions between atoms. MD can accurately predict the properties of materials where models are well developed. For new materials, models may be in their early stages and may lack the ability to produce accurate results; however, MD can still provide insight into the physical properties of these new materials. This thesis will use MD to study two different systems. First, the Lennard-Jones (L-J) liquid is used to study how the intrinsic slip lengths of atomic sized surfaces add to produce an effective slip of a larger surface made up of these atomic constituents. The results show that the effective slip of a surface is dominated by its smallest slip, and these results show good agreement with a theory that predicts effective slip given the intrinsic slip and roughness of a surface. The L-J model is also used to investigate the rolling and sliding motion of viscous drops on super-hydrophobic surfaces. The effects of drop size, slip length, and gravity on drop velocities are investigated, and a model that predicts drop speed given the characteristics of a drop and a surface is proposed. The model shows good agreement with simulation results, especially for certain regimes. Second, graphene is studied with MD using various atomistic models. The energies of layers of graphene are reproduced using an Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) potential, and the energies required to exfoliate graphene from crystal graphite and nickel nano-particles are calculated. The calculations from MD show good agreement with literature and experiment, and these results demonstrate how simple models in MD can produce useful results to aid research and experiment. Finally, the formation of nano-bubbles in graphene grown on platinum is studied using the AIREBO and L-J potentials. The basic formation of graphene nano-bubbles is demonstrated by compressing the edges of graphene flakes. The simulations highlight the importance of proper boundary conditions, such as atom pinning, in order to produce tall, smooth nano-bubbles. The results also suggest that accurate models will be required to effectively demonstrate bubble formation

Team MIT Urban Challenge Technical Report

This technical report describes Team MITs approach to theDARPA Urban Challenge. We have developed a novel strategy forusing many inexpensive sensors, mounted on the vehicle periphery,and calibrated with a new cross-­modal calibrationtechnique. Lidar, camera, and radar data streams are processedusing an innovative, locally smooth state representation thatprovides robust perception for real­ time autonomous control. Aresilient planning and control architecture has been developedfor driving in traffic, comprised of an innovative combination ofwell­proven algorithms for mission planning, situationalplanning, situational interpretation, and trajectory control. These innovations are being incorporated in two new roboticvehicles equipped for autonomous driving in urban environments,with extensive testing on a DARPA site visit course. Experimentalresults demonstrate all basic navigation and some basic trafficbehaviors, including unoccupied autonomous driving, lanefollowing using pure-­pursuit control and our local frameperception strategy, obstacle avoidance using kino-­dynamic RRTpath planning, U-­turns, and precedence evaluation amongst othercars at intersections using our situational interpreter. We areworking to extend these approaches to advanced navigation andtraffic scenarios

Kaʻina Hana ʻŌiwi a me ka Waihona ʻIke Hakuhia Pepa Kūlana

He wahi hoʻomaka kēia pepa kuana no ke Kaʻina Hana ʻŌiwi (KHʻO) a me ka Waihona ʻike Hakuhia (WʻIH) no ka poʻe e ake nei e haku a hana he WʻIK mai ke kuanaʻike kūpono e hoʻokele ʻia nei e ka manaʻo ʻŌiwi. He kiʻina hana ko kēlā a me kēia kaiāulu ʻŌiwi i nā nīnau a mākou e ui aʻe ai. ʻAʻole kēia mea a mākou i kākau ai he pani i ke kūkulu a mālama ʻana i ka pilina kākoʻo kekahi i kekahi me kekahi mau kaiāulu ʻŌiwi. Eia naʻe, hāpai aʻe kēia palapala i kekahi mau manaʻo e noʻonoʻo ai ke komo i kēia mau kamaʻilio ʻana ʻo ka hoʻomaka koho ʻana i ke kuanaʻike ʻŌiwi i ka haku ʻana he waihona ʻike hakuhia. He hoʻāʻo kēia wahi pepa kūlana e hōʻiliʻili i nā ʻano kamaʻilio like ʻole no 20 mahina, no 20 kāʻei hola, no ʻelua hālāwai hoʻonaʻauao, a ma waena hoʻi o kekahi mau poʻe ʻŌiwi (a ʻŌiwi ʻole hoʻi) no nā kaiāulu like ʻole i Aotearoa, Nū Hōlani, ʻAmelika ʻĀkau a me ka Pākīpika. ʻO ke kia nō naʻe, ʻaʻole ʻo ka hoʻolōkahi ʻana he leo. Paʻa nō ka ʻike ʻŌiwi i kekahi mau ʻāina a aupuni kikoʻī a puni ka honua. Hoʻohuli aku kēia mau ʻāina a mōʻaukala like ʻole i nā kaiāulu ʻokoʻa a me ko lākou mau kaʻina hana ʻŌiwi i ke au o ka manawa. ʻAʻohe “kuanaʻike ʻŌiwi hoʻokahi”, a hoʻomau a haku ʻia nā kālaikuhiʻike e ka hoʻokumu ʻana o kekahi mau kaiāulu kikoʻī i loko o kahi mau ʻāina. Ma mua, he hopena ulūlu o ke kālaikuhiʻike a kālaikuhikanaka ko ka loina naʻauao i hoʻāʻo e naʻi a hoʻohilimia i ka loina ʻŌiwi, a hoʻohāiki ʻia ke ʻano o ka manaʻo a kuanaʻike ʻŌiwi. ʻO ko mākou pahuhopu ke kālele ʻana i nā ʻōnaehana ʻike ʻŌiwi like ʻole a me ke ʻano o ka ʻenehana e hāpai i ka nīnau ʻo ka WʻIH. Ma muli o ia palena, a ma kahi o ka hoʻokuʻikuʻi ʻana he manaʻo lōkahi, he hōʻiliʻili kēia pepa kūlana o kēlā ʻano kēia ʻano o ka moʻokalaleo: ʻo nā manaʻo hoʻokele hakulau ʻoe,, ʻo ka ʻatikala akeakamai ʻoe, ʻo ka wehewehena o ka mana ʻenehana mua ʻoe , a ʻo ka poema ʻoe. I ko mākou manaʻo, he ʻolokeʻa kūpono maoli nā leo a kuanaʻike ʻokoʻa i ka ʻoiaʻiʻo he pae kinohi maoli nō kēia kamaʻilio ʻana, a he hōʻike i ka mea heluhelu no nā kuanaʻike i kupu mai i loko o nā hālāwai hoʻonaʻauao

Indigenous Protocol and Artificial Intelligence Position Paper

This position paper on Indigenous Protocol (IP) and Artificial Intelligence (AI) is a starting place for those who want to design and create AI from an ethical position that centers Indigenous concerns. Each Indigenous community will have its own particular approach to the questions we raise in what follows. What we have written here is not a substitute for establishing and maintaining relationships of reciprocal care and support with specific Indigenous communities. Rather, this document offers a range of ideas to take into consideration when entering into conversations which prioritize Indigenous perspectives in the development of artificial intelligence. It captures multiple layers of a discussion that happened over 20 months, across 20 time zones, during two workshops, and between Indigenous people (and a few non-Indigenous folks) from diverse communities in Aotearoa, Australia, North America, and the Pacific. Indigenous ways of knowing are rooted in distinct, sovereign territories across the planet. These extremely diverse landscapes and histories have influenced different communities and their discrete cultural protocols over time. A single ‘Indigenous perspective’ does not exist, as epistemologies are motivated and shaped by the grounding of specific communities in particular territories. Historically, scholarly traditions that homogenize diverse Indigenous cultural practices have resulted in ontological and epistemological violence, and a flattening of the rich texture and variability of Indigenous thought. Our aim is to articulate a multiplicity of Indigenous knowledge systems and technological practices that can and should be brought to bear on the ‘question of AI.’ To that end, rather than being a unified statement this position paper is a collection of heterogeneous texts that range from design guidelines to scholarly essays to artworks to descriptions of technology prototypes to poetry. We feel such a somewhat multivocal and unruly format more accurately reflects the fact that this conversation is very much in an incipient stage as well as keeps the reader aware of the range of viewpoints expressed in the workshops

Molecular Dynamics: A Study on Slip, Drops, and Graphene

Molecular dynamics (MD) is a computational tool used to study physical systems by modeling the atomic-scale interactions between atoms. MD can accurately predict the properties of materials where models are well developed. For new materials, models may be in their early stages and may lack the ability to produce accurate results; however, MD can still provide insight into the physical properties of these new materials. This thesis will use MD to study two different systems. First, the Lennard-Jones (L-J) liquid is used to study how the intrinsic slip lengths of atomic sized surfaces add to produce an effective slip of a larger surface made up of these atomic constituents. The results show that the effective slip of a surface is dominated by its smallest slip, and these results show good agreement with a theory that predicts effective slip given the intrinsic slip and roughness of a surface. The L-J model is also used to investigate the rolling and sliding motion of viscous drops on super-hydrophobic surfaces. The effects of drop size, slip length, and gravity on drop velocities are investigated, and a model that predicts drop speed given the characteristics of a drop and a surface is proposed. The model shows good agreement with simulation results, especially for certain regimes. Second, graphene is studied with MD using various atomistic models. The energies of layers of graphene are reproduced using an Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) potential, and the energies required to exfoliate graphene from crystal graphite and nickel nano-particles are calculated. The calculations from MD show good agreement with literature and experiment, and these results demonstrate how simple models in MD can produce useful results to aid research and experiment. Finally, the formation of nano-bubbles in graphene grown on platinum is studied using the AIREBO and L-J potentials. The basic formation of graphene nano-bubbles is demonstrated by compressing the edges of graphene  flakes. The simulations highlight the importance of proper boundary conditions, such as atom pinning, in order to produce tall, smooth nano-bubbles. The results also suggest that accurate models will be required to effectively demonstrate bubble formation.</p