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Scaling deep learning for materials discovery

Complexity Digest - 1 hour 48 min ago

Amil Merchant, Simon Batzner, Samuel S. Schoenholz, Muratahan Aykol, Gowoon Cheon & Ekin Dogus Cubuk 
Nature (2023)

Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing1,2,3,4,5,6,7,8,9,10,11. From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottlenecked by expensive trial-and-error approaches. Concurrently, deep-learning models for language, vision and biology have showcased emergent predictive capabilities with increasing data and computation12,13,14. Here we show that graph networks trained at scale can reach unprecedented levels of generalization, improving the efficiency of materials discovery by an order of magnitude. Building on 48,000 stable crystals identified in continuing studies15,16,17, improved efficiency enables the discovery of 2.2 million structures below the current convex hull, many of which escaped previous human chemical intuition. Our work represents an order-of-magnitude expansion in stable materials known to humanity. Stable discoveries that are on the final convex hull will be made available to screen for technological applications, as we demonstrate for layered materials and solid-electrolyte candidates. Of the stable structures, 736 have already been independently experimentally realized. The scale and diversity of hundreds of millions of first-principles calculations also unlock modelling capabilities for downstream applications, leading in particular to highly accurate and robust learned interatomic potentials that can be used in condensed-phase molecular-dynamics simulations and high-fidelity zero-shot prediction of ionic conductivity.

Read the full article at: www.nature.com

Evolution and sustainability: gathering the strands for an Anthropocene synthesis

Complexity Digest - Sat, 12/02/2023 - 19:50

compiled and edited by Peter Søgaard Jørgensen, Timothy M. Waring and Vanessa P. Weinberger

How did human societies evolve to become a major force of global change? What dynamics can lead societies on a trajectory of global sustainability? The astonishing growth in human population, economic activity, technological capacity and environmental impact – together known as the Anthropocene – has brought these questions to the fore. In this theme issue, we bring together the major elements of a theory of human evolution and sustainability on Earth. We show how diverse theories and approaches help to understand the past, present and future evolution of the Anthropocene, and discover new opportunities for moving towards sustainability. Collectively, the work provides the basis for an evolutionary synthesis of the human predicament on planet Earth.

Read the Special Issue at: royalsocietypublishing.org

The vulnerability of aging states: A survival analysis across premodern societies

Complexity Digest - Sat, 12/02/2023 - 13:56

The vulnerability of aging states: A survival analysis across premodern societies
Marten Scheffer, Egbert H. van Nes, Luke Kemp, Timothy A. Kohler, Timothy M. Lenton,  and Chi Xu

PNAS 120 (48) e2218834120

Humans become increasingly fragile as they age. We show that something similar may happen to states, although for states, the risk of termination levels off as they grow older, allowing some to persist for millennia. Proximate causes of their demise such as conquest, coups, earthquakes, and droughts are easy to spot and have received significant attention. However, our results suggest that unraveling what shapes resilience to such events is equally important if we are to understand state longevity and collapse. Risk of termination rises over the first 200 y, inviting a search for mechanisms that can undermine resilience at this timescale.

Read the full article at: www.pnas.org

Motile Living Biobots Self‐Construct from Adult Human Somatic Progenitor Seed Cells

Complexity Digest - Sat, 12/02/2023 - 13:51

Gizem Gumuskaya, Pranjal Srivastava, Ben G. Cooper, Hannah Lesser, Ben Semegran, Simon Garnier, Michael Levin

Advanced Science

Fundamental knowledge gaps exist about the plasticity of cells from adult soma and the potential diversity of body shape and behavior in living constructs derived from genetically wild-type cells. Here anthrobots are introduced, a spheroid-shaped multicellular biological robot (biobot) platform with diameters ranging from 30 to 500 microns and cilia-powered locomotive abilities. Each Anthrobot begins as a single cell, derived from the adult human lung, and self-constructs into a multicellular motile biobot after being cultured in extra cellular matrix for 2 weeks and transferred into a minimally viscous habitat. Anthrobots exhibit diverse behaviors with motility patterns ranging from tight loops to straight lines and speeds ranging from 5–50 microns s−1. The anatomical investigations reveal that this behavioral diversity is significantly correlated with their morphological diversity. Anthrobots can assume morphologies with fully polarized or wholly ciliated bodies and spherical or ellipsoidal shapes, each related to a distinct movement type. Anthrobots are found to be capable of traversing, and inducing rapid repair of scratches in, cultured human neural cell sheets in vitro. By controlling microenvironmental cues in bulk, novel structures, with new and unexpected behavior and biomedically-relevant capabilities, can be discovered in morphogenetic processes without direct genetic editing or manual sculpting.

Read the full article at: onlinelibrary.wiley.com

From alternative conceptions of honesty to alternative facts in communications by US politicians

Complexity Digest - Sat, 12/02/2023 - 10:00

Jana Lasser, Segun T. Aroyehun, Fabio Carrella, Almog Simchon, David Garcia & Stephan Lewandowsky
Nature Human Behaviour (2023)

The spread of online misinformation on social media is increasingly perceived as a problem for societal cohesion and democracy. The role of political leaders in this process has attracted less research attention, even though politicians who ‘speak their mind’ are perceived by segments of the public as authentic and honest even if their statements are unsupported by evidence. By analysing communications by members of the US Congress on Twitter between 2011 and 2022, we show that politicians’ conception of honesty has undergone a distinct shift, with authentic belief speaking that may be decoupled from evidence becoming more prominent and more differentiated from explicitly evidence-based fact speaking. We show that for Republicans—but not Democrats—an increase in belief speaking of 10% is associated with a decrease of 12.8 points of quality (NewsGuard scoring system) in the sources shared in a tweet. In contrast, an increase in fact-speaking language is associated with an increase in quality of sources for both parties. Our study is observational and cannot support causal inferences. However, our results are consistent with the hypothesis that the current dissemination of misinformation in political discourse is linked to an alternative understanding of truth and honesty that emphasizes invocation of subjective belief at the expense of reliance on evidence.

Read the full article at: www.nature.com

The nature of epidemic criticality in temporal networks

Complexity Digest - Sat, 12/02/2023 - 00:05

Chao-Ran Cai, Yuan-Yuan Nie, Petter Holme

Analytical studies of network epidemiology almost exclusively focus on the extreme situations where the time scales of network dynamics are well separated (longer or shorter) from that of epidemic propagation. In realistic scenarios, however, these time scales could be similar, which has profound implications for epidemic modeling (e.g., one can no longer reduce the dimensionality of epidemic models). We build a theory for the critical behavior of susceptible-infected-susceptible (SIS) epidemics in the vicinity of the critical threshold on the activity-driven model of temporal networks. We find that the persistence of links in the network leads to increasing recovery rates reducing the threshold. Dynamic correlations (coming from being close to infected nodes increases the likelihood of infection) drive the threshold in the opposite direction. These two counteracting effects make epidemic criticality in temporal networks a remarkably complex phenomenon.

Read the full article at: arxiv.org

The New Quest to Control Evolution

Complexity Digest - Fri, 12/01/2023 - 20:47

Modern scientists aren’t content with predicting how life evolves. They want to shape it.

Read the full article at: www.quantamagazine.org

The Dynamics of Social Interaction Among Evolved Model Agents

Complexity Digest - Fri, 12/01/2023 - 19:50

Haily Merritt, Gabriel J. Severino, Eduardo J. Izquierdo

Artificial Life

We offer three advances to the perceptual crossing simulation studies, which are aimed at challenging methodological individualism in the analysis of social cognition. First, we evolve and systematically test agents in rigorous conditions, identifying a set of 26 “robust circuits” with consistently high and generalizing performance. Next, we transform the sensor from discrete to continuous, facilitating a bifurcation analysis of the dynamics that shows that nonequilibrium dynamics are key to the mutual maintenance of interaction. Finally, we examine agents’ performance with partners whose neural controllers are different from their own and with decoy objects of fixed frequency and amplitude. Nonclonal performance varies and is not predicted by genotypic distance. Frequency-amplitude values that fool the focal agent do not include the agent’s own values. Altogether, our findings accentuate the importance of dynamical and nonclonal analyses for simulated sociality, emphasize the role of dialogue between artificial and human studies, and highlight the contributions of simulation studies to understanding social interactions.

Read the full article at: direct.mit.edu

Beyond the aggregated paradigm: phenology and structure in mutualistic networks

Complexity Digest - Fri, 12/01/2023 - 16:07

Clàudia Payrató-Borràs, Carlos Gracia-Lázaro, Laura Hernández, Yamir Moreno

Mutualistic interactions, where species interact to obtain mutual benefits, constitute an essential component of natural ecosystems. The use of ecological networks to represent the species and their ecological interactions allows the study of structural and dynamic patterns common to different ecosystems. However, by neglecting the temporal dimension of mutualistic communities, relevant insights into the organization and functioning of natural ecosystems can be lost. Therefore, it is crucial to incorporate empirical phenology — the cycles of species’ activity within a season — to fully understand the effects of temporal variability on network architecture. In this paper, by using two empirical datasets together with a set of synthetic models, we propose a framework to characterize phenology on ecological networks and assess the effect of temporal variability. Analyses reveal that non-trivial information is missed when portraying the network of interactions as static, which leads to overestimating the value of fundamental structural features. We discuss the implications of our findings for mutualistic relationships and intra-guild competition for common resources. We show that recorded interactions and species’ activity duration are pivotal factors in accurately replicating observed patterns within mutualistic communities. Furthermore, our exploration of synthetic models underscores the system-specific character of the mechanisms driving phenology, increasing our understanding of the complexities of natural ecosystems.

Read the full article at: arxiv.org

What Is in a Simplicial Complex? A Metaplex-Based Approach to Its Structure and Dynamics

Complexity Digest - Fri, 12/01/2023 - 13:49

Manuel Miranda, Gissell Estrada-Rodriguez, and Ernesto Estrada

Entropy 2023, 25(12), 1599

Geometric realization of simplicial complexes makes them a unique representation of complex systems. The existence of local continuous spaces at the simplices level with global discrete connectivity between simplices makes the analysis of dynamical systems on simplicial complexes a challenging problem. In this work, we provide some examples of complex systems in which this representation would be a more appropriate model of real-world phenomena. Here, we generalize the concept of metaplexes to embrace that of geometric simplicial complexes, which also includes the definition of dynamical systems on them. A metaplex is formed by regions of a continuous space of any dimension interconnected by sinks and sources that works controlled by discrete (graph) operators. The definition of simplicial metaplexes given here allows the description of the diffusion dynamics of this system in a way that solves the existing problems with previous models. We make a detailed analysis of the generalities and possible extensions of this model beyond simplicial complexes, e.g., from polytopal and cell complexes to manifold complexes, and apply it to a real-world simplicial complex representing the visual cortex of a macaque.

Read the full article at: www.mdpi.com

Beehive scale-free emergent dynamics

Complexity Digest - Thu, 11/30/2023 - 10:58

Ivan Shpurov, Tom Froese, Dante R. Chialvo

It has been repeatedly reported that the collective dynamics of social insects exhibit universal emergent properties similar to other complex systems. In this note, we study a previously published data set in which the positions of thousands of honeybees in a hive are individually tracked over multiple days. The results show that the hive dynamics exhibit long-range spatial and temporal correlations in the occupancy density fluctuations, despite the characteristic short-range bees’ mutual interactions. The variations in the occupancy unveil a non-monotonic function between density and bees’ flow, reminiscent of the car traffic dynamic near a jamming transition at which the system performance is optimized to achieve the highest possible throughput. Overall, these results suggest that the beehive collective dynamics are self-adjusted towards a point near its optimal density.

Read the full article at: arxiv.org

Cohesion: A Measure of Organisation and Epistemic Uncertainty of Incoherent Ensembles

Complexity Digest - Thu, 11/30/2023 - 08:57

Timothy Davey

Entropy 2023, 25(12), 1605

This paper offers a measure of how organised a system is, as defined by self-consistency. Complex dynamics such as tipping points and feedback loops can cause systems with identical initial parameters to vary greatly by their final state. These systems can be called non-ergodic or incoherent. This lack of consistency (or replicability) of a system can be seen to drive an additional form of uncertainty, beyond the variance that is typically considered. However, certain self-organising systems can be shown to have some self-consistency around these tipping points, when compared with systems that find no consistent final states. Here, we propose a measure of this self-consistency that is used to quantify our confidence in the outcomes of agent-based models, simulations or experiments of dynamical systems, which may or may not contain multiple attractors.

Read the full article at: www.mdpi.com

A network-based normalized impact measure reveals successful periods of scientific discovery across disciplines

Complexity Digest - Wed, 11/29/2023 - 13:59

Qing Ke, Alexander J. Gates, and Albert-László Barabási

PNAS 120 (48) e2309378120

Distinct citation practices across time and discipline limit our ability to compare different scientific achievements. For example, raw citation counts suggest that advancements in biomedical research have consistently overshadowed the accomplishments from all other disciplines. Here, we introduce a network-based methodology for normalizing citation counts that mitigates the effects of temporal and disciplinary variations in citations. The method allows us to highlight successful periods of scientific discovery across the disciplines and provides insights into the evolution of science.

Read the full article at: www.pnas.org

EPJ B Topical Issue: Advances in Complex Systems

Complexity Digest - Wed, 11/29/2023 - 10:15

Guest Editors: Thiago B. Murari, Marcelo A. Moret, Hernane B. de B. Pereira, Tarcísio M. Rocha Filho, José F. F. Mendes and Tiziana Di Matteo

Submissions are invited for a Topical Issue of EPJ B on Advances in Complex Systems.

In general, a complex system is a system composed of many components that may interact with each other in nonlinear ways. Examples of complex systems include fractals, chaos, nonlinear dynamics, self-organized criticality, and complex networks. This issue aims to promote research in complex systems, both in pure and applied contexts.

The goal of this topical issue is to summarize the most recent research, covering aspects such as:

Foundations of Complex Systems, Basic Sciences, and Quantum Complexity
Complex Networks
Data Science, Machine Learning, and Artificial Intelligence in the context of Complex Systems
Computation and Information Processing in Complex Systems
Economics and Finance
Social Systems
Ecological Systems
Cognition, Psychology, and Neurosciences
Complexity in Biology and Health Sciences
City Science, Mobility, and Transport
Energy, Environment, Sustainability, Climate, and Global Change

Read the full call at: epjb.epj.org

Self-Organisation of Prediction Models

Complexity Digest - Tue, 11/28/2023 - 12:25

Rainer Feistel

Entropy 2023, 25(12), 1596

Living organisms are active open systems far from thermodynamic equilibrium. The ability to behave actively corresponds to dynamical metastability: minor but supercritical internal or external effects may trigger major substantial actions such as gross mechanical motion, dissipating internally accumulated energy reserves. Gaining a selective advantage from the beneficial use of activity requires a consistent combination of sensual perception, memorised experience, statistical or causal prediction models, and the resulting favourable decisions on actions. This information processing chain originated from mere physical interaction processes prior to life, here denoted as structural information exchange. From there, the self-organised transition to symbolic information processing marks the beginning of life, evolving through the novel purposivity of trial-and-error feedback and the accumulation of symbolic information. The emergence of symbols and prediction models can be described as a ritualisation transition, a symmetry-breaking kinetic phase transition of the second kind previously known from behavioural biology. The related new symmetry is the neutrally stable arbitrariness, conventionality, or code invariance of symbols with respect to their meaning. The meaning of such symbols is given by the structural effect they ultimately unleash, directly or indirectly, by deciding on which actions to take. The early genetic code represents the first symbols. The genetically inherited symbolic information is the first prediction model for activities sufficient for survival under the condition of environmental continuity, sometimes understood as the “final causality” property of the model.

Read the full article at: www.mdpi.com

9 Faculty Openings | Systems Science and Industrial Engineering | Binghamton University

Complexity Digest - Tue, 11/28/2023 - 10:05

The Department of Systems Science and Industrial Engineering (SSIE) at Binghamton University’s Thomas J. Watson College of Engineering and Applied Science is expanding further and seeks eight (8) tenure-track faculty:

Assistant Professor in Energy Systems (1 position)
Assistant Professor in Health Systems (1 position)
Assistant Professor in Systems Science (1 position)
Associate Professor of Nanofabrication (1 position)
Associate or Full Professor in Energy Storage (1 position)
Associate or Full Professor in Energy Systems and Policy (1 position)
Associate or Full Professor in Flexible, Additive and Hybrid Electronic Systems (1 position)
Associate or Full Professor in Systems Engineering in Electronics and Semiconductor Manufacturing (1 position)
In addition, the greater Watson College of Engineering and Applied Science also has openings for the following roles, which could also have close association with SSIE depending on candidate background:

AI/ML SUNY Empire Innovation Professor (1 position)

Read the full article at: www.binghamton.edu

Impact of physicality on network structure

Complexity Digest - Sun, 11/19/2023 - 12:23

Márton Pósfai, Balázs Szegedy, Iva Bačić, Luka Blagojević, Miklós Abért, János Kertész, László Lovász & Albert-László Barabási 

Nature Physics (2023)

The emergence of detailed maps of physical networks, such as the brain connectome, vascular networks or composite networks in metamaterials, whose nodes and links are physical entities, has demonstrated the limits of the current network science toolset. Link physicality imposes a non-crossing condition that affects both the evolution and the structure of a network, in a way that the adjacency matrix alone—the starting point of all graph-based approaches—cannot capture. Here, we introduce a meta-graph that helps us to discover an exact mapping between linear physical networks and independent sets, which is a central concept in graph theory. The mapping allows us to analytically derive both the onset of physical effects and the emergence of a jamming transition, and to show that physicality affects the network structure even when the total volume of the links is negligible. Finally, we construct the meta-graphs of several real physical networks, which allows us to predict functional features, such as synapse formation in the brain connectome, that agree with empirical data. Overall, our results show that, to understand the evolution and behaviour of real complex networks, the role of physicality must be fully quantified.

Read the full article at: www.nature.com

Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks

Complexity Digest - Fri, 11/17/2023 - 14:23

Melanie Mitchell, Alessandro B. Palmarini, Arseny Moskvichev

We explore the abstract reasoning abilities of text-only and multimodal versions of GPT-4, using the ConceptARC benchmark [10], which is designed to evaluate robust understanding and reasoning with core-knowledge concepts. We extend the work of Moskvichev et al. [10] by evaluating GPT-4 on more detailed, one-shot prompting (rather than simple, zero-shot prompts) with text versions of ConceptARC tasks, and by evaluating GPT-4V, the multimodal version of GPT-4, on zero- and one-shot prompts using image versions of the simplest tasks. Our experimental results support the conclusion that neither version of GPT-4 has developed robust abstraction abilities at humanlike levels.

Read the full article at: arxiv.org

The renormalization group

Complexity Digest - Fri, 11/17/2023 - 12:23

A Focus issue celebrating the 50th anniversary of Kenneth Wilson’s work on the renormalization group.

More at: www.nature.com

Network Information Dynamics Renormalization Group

Complexity Digest - Thu, 11/16/2023 - 12:25

Zhang Zhang, Arsham Ghavasieh, Jiang Zhang, Manlio De Domenico

Information dynamics is vital for many complex systems with networked backbones, from cells to societies. Recent advances in statistical physics have enabled capturing the macroscopic network properties, like how diverse the flow pathways are and how fast the signals can transport, based on the network counterparts of entropy and free energy. However, given the computational challenge posed by the large number of components in real-world systems, there is a need for advanced network renormalization— i.e., compression— methods providing simpler-to-read representations while preserving the flow of information between functional units across scales. We use graph neural networks to identify suitable groups of components for coarse-graining a network and achieve a low computational complexity suitable for practical application. Even for large compressions, our approach is highly effective in preserving the flow in synthetic and empirical networks, as demonstrated by theoretical analysis and numerical experiments. Remarkably, we find that the model works by merging nodes of similar ecological niches— i.e., structural properties—, suggesting that they play redundant roles as senders or receivers of information. Our work offers a low-complexity renormalization method breaking the size barrier for meaningful compressions of extremely large networks, working as a multiscale topological lens in preserving the flow of information in biological, social, and technological systems better than existing alternatives mostly focused on structural properties of a network.

Read the full article at: www.researchsquare.com


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