I would spend the $50 million assembling a distributed molecular machine research program composed of multiple teams.
High-Level Budget Breakdown
$25 Million: Theoretical and Computational Research Grants
$25 Million: Experimental Research Grants
On Theoretical and Computational Research
The $25 million in grants would, over a period of 5 years, support 5 teams. Each team would pursue a distinct subfield of molecular machine research that addresses an outstanding question in molecular machine development. Candidate questions could include:
Basic Science and Engineering Studies
Theoretical research in molecular machines could involve either:
These two approaches are distinct in their objectives and methods, but can nonetheless complement one another. (Drexler 2004, 3)
Exploratory Engineering and Amenability to Experimentation
Successful research will, for each subfield of molecular machine research, yield a subfield roadmap that refines and articulates a pathway with specified design objectives by which a track of molecular machine development can be defined. The subfield roadmaps should help to foster research and design strategies for redressing key long-term challenges in molecular machine development. The stipends should allow researchers the latitude to study advanced molecular machine capabilities to a degree often inhibited by the pressures of specialization and short-termism imposed by research funders. The objective of the research would be the advancement of “exploratory engineering” studies in molecular machine design that extend to long-term aspirations. (See Drexler 1988) The roadmaps would build upon the 2007 roadmap (see Drexler et al. 2007) but differ therefrom in the following ways:
Importantly, the short-term spectrum of the roadmap should be compatible with contemporary experimental capabilities such that the relevant designs are amenable to experimental testing.
On Experimental Research
After the second year, two of the research teams with results most amenable to experimentation would be invited to augment their team to include researchers with experimental expertise and facilities in order to subject the short-term specifications of the roadmap to experimental testing. These teams could revise their theoretical and computational models during the fourth year and conduct a second wave of experimentation during the fifth year.
The literature on molecular machines introduces a number of important criteria that correspond to theoretical capabilities with the potential to generate transformative outcomes. However, it is common for a given paper to be devoted to a singular criterion. Although many proposed capabilities have been theoretically introduced, few papers compare capabilities. Even fewer analyze their compatibility and systemic integrability. Subsuming many capabilities under core criteria, I would nominate the following as the most crucial:
For each of these criteria, a number of capabilities and performance measures can be identified in the literature. The relative advantages of such capabilities and performance measures, as well as potential tradeoffs therebetween, should be better ascertained. A non-exhaustive list of capabilities and performance measures are included below:
Fabrication and Manufacturing
The development of a sizable inventory of nanoparts and molecular machine devices (differential and universal gears, bearings, pumps, tooltips, etc.), first beginning in the late 1980s (see Drexler 1987), has been underway for over three decades. And yet, it still remains unclear to what extent compatibilities permit interoperationality between, and systemic integration of, such devices. What has not yet been done, and needs to be done, is the completion of systems design studies for integrating nanoparts. “Larger systems call for multi-scale modeling techniques in which atomistic descriptions support continuum and lumped-component models that describe extended structures and the interaction of subsystems.” (Drexler 2006, 10) The design of larger systems will also require, in turn, the design of larger components: “Atomically precise nanoparts, once fabricated, must be transferred from the fabrication site and assembled into atomically precise complex components containing many nanoparts. Such components may include gear trains in housings, sensors, motors, manipulator arms, power generators, and computers.” (Freitas 2011, 395)
Alternatively, a molecular machine can be defined systemically as a constituent part of a “machine phase system”, a propositional phase of matter in which the components are “eutactic”, or finely arranged. (See Drexler 1992, 6; 2001, 74)
High performance computing can greatly assist the exploration of large configuration spaces in designing molecular machines. Although computational costs are rarely reported in papers on theoretical molecular nanotechnology, they are indeed occasionally reported. In an important paper published by Freitas and Merkle on tooltip design, it is reported that over 100,000 hours of computation was required to conduct the research. (See Freitas and Merkle 2008, 768) Although I am hesitant to extrapolate from this single paper, I do suspect that, if over 100,000 hours of computation is required for tooltip design, simulations of subsystems may require high-performance computing.
Not Enough: Theoretical Nanotechnology
Too Much: Biochemistry
Admittedly, the study of biological molecular machines is inspirational, edifying, and instructive. However, undue focus on such molecular machines may bias molecular machine researchers towards emulation of such machines or the design of homological devices. The literature on theoretical molecular machines suggests that mechanosynthesis, nanofabrication, and nanocomputation can yield machines without precedent in contemporary biology: “Nature exhibits prototypes of simple productive nanosystems, but physical analysis indicates that artificial systems can go far beyond the biological model.” (Drexler 2005, 339)
In my view, the best question that can be asked is:
“How can researchers contribute towards a generalizable molecular machine capability?”
The question of generalizable research is key because it anchors the field of molecular machine research with reference to the foundational engineering objective that first motivated the field. We can recall that molecular machines were first described in Proceedings of the National Academy of Sciences as “a general molecular engineering technology.” (Drexler 1981, 5275) The notion of generalizability is ideally resistant to departmental/disciplinary specialization and narrow tasks. Instead, a generalizable capability assists the entirety of the molecular machine field.
Some additional remarks can be made on generalizability. An important analogue is the research question of generalizability in artificial intelligence (AI) studies. Researchers pursuing artificial general intelligence (AGI) attempt to develop theories and models of generalizable intelligence with capabilities intended to greatly exceed contemporary ‘narrow AI’ services. (e.g. Goertzel and Pennachin 2007, 1)
I would contend that molecular machine research requires its own analogue of AGI (perhaps, ‘General Molecular Machinery’, or ‘GMM’). A researcher contributing to GMM might produce knowledge, either in basic science or engineering, that fosters an understanding of molecular dynamics and design prospects to which many researchers can refer. Alternatively, a GMM researcher might help to develop a fabrication or computational capability that can be applied to the design of many machines.
Mathematical modeling techniques, including those involving group theory, representation theory, and category theory, can be used to formalize molecular structures in simplified form according to symmetries and structural equivalences. Such techniques are ‘simple’ both inasmuch as they are inexpensive and insofar as that they yield representations that compress potentially complex molecular components and systems into wieldy, formal expressions.
We can cite a precedent case for such work. Merkle (1993) introduces a mathematical proof of the isomorphism of potential energy levels of molecular bearings from the first principles of bearing symmetry. We can expand and improve upon this particular approach by using group representation theory and proof assistants such as Lean or Coq. Mathematical techniques may prove to be useful for designers who seek to decompose their designs into subcomponents (Hogg 1999, 305-306) or understand how such subsystems might compose larger systems. (Drexler 2006, 10) Zimmer (1990) describes such a technique (the application of category theory and group theory to decompositional and compositional models) as “representation engineering”.
I have seen many reports in which silicon and germanium play important roles. In certain instances, germanium has been identified as possessing particular qualities that render it uniquely suitable for certain applications. For instance, germanium has been nominated as the optimal element for the design of a hydrogen donation tool, given the (preferable) weakness of the bonds that Ge forms with H. (Freitas 2011, 393) To take another example, Freitas et al. (2007) employ germanium atoms for holding C2 dimers in tooltips. Therefore, at this stage, it is not clear (at least to me) whether or not silicon will prove to be used exclusively without other heavy carbon group elements.
With respect to bootstrapping diamondoid structures, I keep in mind that the number of possible theoretical diamondoid structures that can occupy a given cubic volume is astronomical. It is sometimes cited that 10148 different diamondoid structures per cubic nanometer are possible. (Drexler 1995, 326) However, this estimate is produced with adherence to some conservatism, which, if suspended, yields an estimate of 10217 per cubic nanometer. (Drexler 1992, 264) This second estimate is obtained by first calculating the number of configuration space options for a diamondoid structure from the entropy of fusion for silicon and germanium, which are roughly equal. (ibid, 263-264) Thus, the vastness of the theoretical combinatoria for Si and Ge diamondoid do not differ. Given such an astronomical number of combinations, “the probability that modern technology can fabricate a particular randomly picked structure from the set defined above is effectively zero.” (Drexler 1995, 326)
Thus, when considering the prospect of diamondoid bootstrapping, combinatorial explosivity will become manifest as we entertain different design choices. For instance, “[a] positionally controlled dimer could be bonded at many different sites on a growing diamondoid workpiece, in principle allowing the construction of a wide variety of useful nanopart shapes.” (Freitas 2011, 392-393) Therefore — if you will forgive my conservatism on this point — I will opine that further exploratory engineering and experimentation in diamondoid mechanosynthesis (DMS) must be conducted before the question of silicon can be settled.
Ahuja, S., Singh, G., Bhaduri, D., and Shukla, S. “Fault- and Defect-Tolerant Architecture for Nanocomputing.” In Bio-Inspired and Integrated Nanocomputing, edited by M.M. Eshaghian-Wilner. Hoboken: Wiley, 2009.
Drexler, K.E. “Molecular engineering: An approach to the development of general capabilities for molecular manipulation,” Proceedings of the National Academy of Sciences 78, No. 9 (1981): 5275-5278.
____. 1987. “Nanomachinery: atomically precise gears and bearings,” Proc. IEEE Micro Robot. Teleoperators Workshop. Hyannis, Massachusetts.
____. “Exploratory Engineering.” The Foresight Institute. 1988. https://legacy.foresight.org/Updates/Background3.php#ExplorEng
____. “Rod logic and thermal noise in the mechanical nanocomputer.” In Molecular Electronic Devices: Proceedings of the 3rd International Symposium on Molecular Electronic Devices Arlington, Virginia, 6-8 October, 1986, ed. F.L. Carter, R. Siatkowski, and H. Wohltjen. Amsterdam: North-Holland, 1989.
____. “Molecular tip arrays for molecular imaging and nanofabrication,” Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures Processing, Measurement, and Phenomena 9, No. 2 (1991): 1394-1397.
____. “Molecular directions in nanotechnology,” Nanotechnology 2 (1991): 113-118.
____. Nanosystems: Molecular Machinery, Manufacturing, and Computation. New York: John Wiley & Sons, 1992.
____. “Molecular Manufacturing: Perspectives on the Ultimate Limits of Fabrication,” Philosophical Transactions: Physical Sciences and Engineering 353, No. 1703 (1995): 323-331.
____. “Machine-Phase Nanotechnology,” Scientific American (2001): 74-75.
____. “Productive nanosystems: the physics of molecular fabrication,” Physics Education 40, No. 4 (2005): 339-346. “Productive nanosystems: A technology roadmap.” 2007. Edited by K.E. Drexler, J. Randall, S. Corchnoy, A. Kawczak, and M.L. Steve. Battelle Memorial Institute, Columbus, OH; Foresight Nanotech Institute, Palo Alto, CA.
Freitas, R.A. “Economic Impact of the Personal Nanofactory,” Nanotechnology Perceptions 6 (2006): 1-16.
____. “Diamondoid Mechanosynthesis for Tip-Based Nanofabrication.” In Tip-Based Nanofabrication, edited by A.A. Tseng. New York: Springer, 2011. Freitas, R.A., Allis, D.G., and Merkle, R.C. “Horizontal Ge-Substituted Polymantane-Based C2 Dimer Placement TooltipMotifs for Diamond Mechanosynthesis,” Journal of Theoretical Nanoscience 4, No. 3 (2007): 1-10. Freitas, Robert A., and Ralph C. Merkle. Kinematic Self-Replicating Machines. Georgetown: Landes Bioscience, 2004.
____. “A minimal toolset for positional diamond mechanosynthesis,” Journal of computational and theoretical nanoscience 5, No. 5 (2008): 760-861. Artificial General Intelligence, edited by B. Goertzel and C. Pennachin. Heidelberg: Springer, 2007. Hall, J.S. “An electroid switching model for reversible computer architectures.” In Proceedings of Physics of Computation Workshop, Dallas Texas. 1992.
____. “Nanocomputers and reversible logic,” Nanotechnology 5, no. 3 (1994): 157-167.
____. “A reversible instruction set architecture and algorithms.” In Proceedings Workshop on Physics and Computation. PhysComp’94. 1994.
Hogg, T. “Robust self-assembly using highly designable structures,” Nanotechnology 10 (1999): 300-307.
Merkle, R.C. “Computational nanotechnology,” Nanotechnology 2 (1991): 134-141
____. “Self Replicating Systems and Molecular Manufacturing,” Journal of the British Interplanetary Society 45 (1992): 407-413.
____. “A proof about molecular bearings,” Nanotechnology 4 (1993): 86-90.
____. “Convergent assembly,” Nanotechnology 8 (1997): 18-22.
____. “A new family of six degrees of freedom positional devices,” Nanotechnology 8 (1997): 47-52.
____. “Molecular building blocks and development strategies for molecular nanotechnology,” Nanotechnology 11 (2000): 89-99.
Merkle, R. C., Freitas, R. A., Hogg, T., Moore, T. E., Moses, M. S., and Ryley, J. “Mechanical Computing Systems Using Only Links and Rotary Joints,” Journal of Mechanisms and Robotics 10, No. 6 (2018).
Musgrave, C. B., Perry, J.K., Merkle, R.C., and Goddard, W.A. “Theoretical studies of a hydrogen abstraction tool for nanotechnology,” Nanotechnology 2, No. 4 (1991): 187-195.
Phoenix, C., and Drexler, K.E. “Safe Exponential Manufacturing,” Nanotechnology 15 (2004): 869-872.
Skidmore, G.D., Parker, E., Ellis, M., Sarkar, N., and Merkle, R. “Exponential assembly,” Nanotechnology 12, No. 3 (2001): 316-321.
Zimmer R.M. “Representation Engineering and Category Theory.” In Change of Representation and Inductive Bias, edited by D.P. Benjamin. Boston: Springer, 1990.
Boyd is a Complex Systems Analyst at SingularityNET, a Project Research Assistant at Wolfram Research, and a Research Affiliate at the Wolfram Physics Project. Additionally, he is, with the support of Prince Mohammad Bin Fahd University and the World Future Studies Federation, conducting a design study in urban-scale molecular machine nanosystems. Boyd has been involved with institutions such as the National Defense University, the SETI Institute, the Millennium Project, and the Parsons School of Design. He resides in Mountain View, California.