In the rapidly evolving domain of artificial intelligence for materials science, path dependency remains a critically overlooked phenomenon that shapes both computational pipelines and the broader scientific enterprise. Algorithmic path dependency manifests when seemingly innocuous early choices in neural network initialization, training data ordering, hyperparameter selection, feature descriptor definition, or early stopping criteria create irreversible constraints on subsequent model behaviors and outputs, as evidenced in recurrent neural network architectures designed for heterogeneous materials. Scientific path dependency, by contrast, arises in the history and philosophy of science when initial decisions regarding research problems, material systems, theoretical frameworks, experimental protocols, or funding priorities lock research communities into particular trajectories, rendering alternative avenues increasingly difficult to pursue even when they might yield superior insights. This paper advances the theoretical claim that algorithmic path dependency propagates directly into scientific path dependency within materials AI, such that technical decisions made at the level of code and data become de facto determinants of which materials are discovered, which questions are asked, and which knowledge ultimately enters the scientific canon. The linkage operates through identifiable mechanisms, including output filtering, resource allocation, knowledge representation, and publication bias, each amplifying the long-term scientific consequences of early algorithmic commitments. By drawing upon foundational economic concepts of increasing returns and historical contingency alongside contemporary literature in machine learning for materials, this theoretical analysis proposes that materials AI researchers must explicitly recognize these dynamics to avoid unintended lock-in effects that could limit the diversity and robustness of future discoveries. The analysis further derives corollaries concerning constrained output diversity, the practical irreversibility of certain scientific paths, and the necessity of methodological pluralism, offering concrete implications for research practice, peer review standards, and community norms. Ultimately, this conceptual linkage reframes early algorithmic decisions not as mere technical details but as foundational scientific commitments whose consequences reverberate through the entire materials discovery ecosystem.
Materials AI has transformed the landscape of materials discovery by enabling rapid screening of vast compositional spaces, prediction of novel structures, and guidance of experimental efforts toward promising candidates [1-5]. Yet beneath the surface of these impressive capabilities lies a fundamental issue that has received insufficient theoretical attention: the role of path dependency. When researchers select a particular dataset, initialize a neural network with random weights, choose specific hyperparameters, define material descriptors in one way rather than another, or apply early stopping rules during training, these decisions are frequently treated as purely technical or arbitrary matters of implementation convenience [6-11]. In reality, however, such choices establish trajectories that become self-reinforcing and increasingly resistant to reversal, even when subsequent evidence suggests superior alternatives might exist.
This paper argues that algorithmic path dependency—the phenomenon whereby early computational choices constrain future algorithmic possibilities—directly translates into scientific path dependency, whereby early research decisions constrain future scientific directions in materials discovery. The problem is not merely that different initializations might produce slightly different models; rather, the very questions scientists can ask, the materials they choose to investigate experimentally, and the knowledge they ultimately generate are shaped by these upstream algorithmic commitments [3, 12-14]. For instance, if an active learning pipeline prioritizes certain compositional regions based on an initial random seed or data ordering, entire families of materials may remain unexplored, not because they lack promise but because they were never surfaced by the algorithm [7].
The consequences of ignoring this linkage are profound. Materials AI is increasingly positioned as an autonomous or semi-autonomous driver of discovery [7], yet without a conceptual framework for understanding how code-level decisions become science-level constraints, the field risks locking itself into narrow technological and epistemic paths. Early decisions in model architecture or descriptor choice do not simply affect predictive accuracy in isolation; they determine which phenomena can be represented, which hypotheses can be tested, and which breakthroughs can be recognized as such [13, 15-18]. This theoretical analysis, therefore, seeks to make path dependency visible as a core feature rather than a peripheral bug in materials AI systems.
By tracing the origins of path dependency concepts from economics and the history of technology [1, 2], defining algorithmic and scientific variants with precision, establishing their linkage through explicit mechanisms, deriving logical corollaries, and outlining practical implications, the present work offers a conceptual scaffold for more reflexive practice in the field. The analysis proceeds without reliance on new empirical data, simulations, or performance metrics, focusing instead on logical elaboration, conceptual distinctions, and illustrative examples drawn from the existing literature. In doing so, it proposes that seemingly neutral technical choices in AI pipelines carry scientific weight comparable to the selection of research questions or theoretical paradigms [3, 19-23]. Recognition of this reality is essential if materials AI is to fulfill its promise of accelerating discovery without inadvertently narrowing the scope of what can be discovered.
Path dependency has long been recognized as a fundamental property of complex dynamic systems in which history matters in a non-trivial way.
Path dependency is a property of processes in which early events or decisions disproportionately constrain subsequent outcomes, creating forms of lock-in that persist even when superior alternatives become available later.
This definition draws directly from foundational work in economics and the history of technology. Arthur demonstrated how increasing returns—situations in which the benefits of adopting a technology or standard grow with the number of adopters—can lead to lock-in effects where an initially dominant but ultimately inferior option becomes entrenched [1]. Similarly, David illustrated the persistence of the QWERTY keyboard layout despite the existence of ergonomically superior alternatives, showing how small historical contingencies combined with switching costs can produce long-term inefficiency [2].
Key concepts associated with path dependency include increasing returns, switching costs, lock-in, and historical contingency. Increasing returns refer to positive feedback loops whereby the adoption of one option makes its continued dominance more likely. Switching costs encompass the economic, cognitive, or infrastructural expenses required to change paths once established. Lock-in describes the resulting state of rigidity in which change becomes difficult or impossible without external disruption. Historical contingency emphasizes that outcomes depend not only on current conditions but on the specific sequence of past events, rendering the process non-ergodic—different sequences can lead to different equilibria even under identical starting conditions.
These concepts have proven generative beyond economics, finding application in technology studies, institutional theory, and, more recently, in computational domains. In the present context, they provide a lens for understanding why early choices in materials AI pipelines can exert outsized influence on downstream scientific outcomes. The non-ergodic nature of path-dependent processes implies that two otherwise identical research programs employing different initializations or data orderings may converge on fundamentally different material discoveries and theoretical understandings, even when both appear equally “optimal” by conventional metrics [3].
Importantly, path dependency is not synonymous with mere inertia or randomness. It involves genuine sensitivity to initial conditions coupled with self-reinforcing mechanisms that amplify small differences over time. This distinguishes it from stochastic variation that averages out across multiple runs. In path-dependent systems, the amplification is structural: early choices reshape the possibility space itself, foreclosing certain futures while enabling others [1]. The remainder of this analysis applies these foundational ideas to algorithmic and scientific domains within materials AI, demonstrating how economic and historical concepts illuminate contemporary computational practices in materials discovery.
Building upon the general concept of path dependency, algorithmic path dependency refers specifically to the constraining effects of early computational choices within machine learning pipelines.
Algorithmic path dependency occurs when initial decisions regarding model architecture, parameter initialization, data sequencing, hyperparameter configuration, or training termination criteria create self-reinforcing trajectories that limit the set of reachable model states and outputs, rendering later corrections costly or infeasible.
At least five primary sources contribute to algorithmic path dependency in materials AI contexts.
Random initialization of neural network weights. Even identical architectures trained on the same data can converge to qualitatively different internal representations when initialized differently, particularly in non-convex optimization landscapes typical of deep learning for materials properties [8, 9].
Ordering of training data. The sequence in which examples are presented during stochastic gradient descent influences gradient trajectories and final model parameters; in materials datasets characterized by compositional imbalance, early exposure to certain chemistries can bias the learned embedding space [12, 18].
Choice of hyperparameters. Learning rate schedules, regularization strengths, or batch sizes selected at the outset establish optimization dynamics that become difficult to escape without full retraining, effectively locking the model into particular generalization behaviors [10, 11].
Selection of features or descriptors. Once a specific set of material descriptors or graph representations is chosen, the model’s inductive bias is fixed; alternative physical or chemical encodings that might reveal different structure-property relationships are structurally excluded from consideration [5, 13].
Early stopping or convergence criteria. Decisions about when to terminate training based on validation metrics made early in development can prevent exploration of flatter minima or more generalizable solutions that emerge only after prolonged optimization [15, 24].
These sources interact multiplicatively. For example, a particular random initialization combined with a specific data ordering can amplify sensitivity to hyperparameter choices, creating compound lock-in effects observable in recurrent neural networks applied to path-dependent mechanical responses in heterogeneous materials [8, 25-28]. The consequences are not merely quantitative differences in accuracy but qualitative differences in the model’s representational capacity: certain material phenomena may be accurately captured. In contrast, others remain invisible or poorly modeled [12].
Literature on physically recurrent neural networks for path-dependent materials explicitly acknowledges sensitivity to initialization and training protocols, demonstrating that different starting conditions yield distinct predictions of history-dependent behavior even when loss functions are identical [8, 28]. Similarly, deep learning approaches to plasticity prediction reveal that data ordering profoundly affects the learned constitutive relations, with early exposure to certain strain paths locking the network into specific deformation regimes [12]. These examples illustrate how algorithmic path dependency operates as a structural feature rather than a transient noise that can be averaged away through ensemble methods alone. Once established, the path constrains not only predictive performance but the very hypotheses the model can support when deployed in discovery workflows [3].
Scientific path dependency extends the logic of historical contingency to the epistemic practices of research communities.
Scientific path dependency arises when initial choices of research problems, material systems, theoretical frameworks, experimental techniques, or publication and funding priorities create self-reinforcing trajectories that constrain the questions asked, the evidence considered, and the knowledge ultimately produced, even when alternative paths might prove more fruitful.
At least five primary sources fuel scientific path dependency in materials science and related fields.
Choice of research problem. Early selection of particular phenomena or material classes directs collective attention and resources, making subsequent shifts to unrelated systems cognitively and institutionally expensive [19, 23].
Selection of materials to study. Focusing experimental or computational efforts on a narrow compositional space—often guided by initial algorithmic suggestions—renders broader exploration less likely over time [7, 14].
Once a community standardizes around specific characterization methods or synthesis routes, alternative techniques become marginalized, limiting the types of data that enter the evidentiary base [20, 24, 25].
Theoretical frameworks employed. Adoption of particular modeling paradigms or descriptor sets early on shapes the conceptual vocabulary and explanatory strategies available to subsequent researchers [6, 13].
Publication and funding decisions. Successful initial results in one direction generate citations, grants, and visibility that reinforce the same trajectory while alternative approaches remain unpublished or unfunded [19, 23].
These sources interact through feedback loops between community practices and individual research programs. Historical contingency studies in philosophy of science emphasize how small early differences in problem framing can lead to divergent research programs that appear incommensurable years later [23]. In materials AI specifically, the integration of machine learning into discovery pipelines introduces new vectors for such contingency: algorithmic recommendations that prioritize certain candidates shape which materials receive experimental validation, which in turn influences future funding calls and publication norms [7, 14].
Literature on the history of artificial intelligence highlights parallel dynamics in which early architectural choices and benchmark selections have locked entire subfields into particular evaluation criteria and research questions [19]. Analogous contingency effects appear in ecological and evolutionary studies where historical sequence determines community assembly outcomes [20, 21, 25]. Within materials contexts, these patterns manifest when initial algorithmic outputs bias the selection of systems for inverse design, thereby constraining the functional properties ultimately targeted [6]. The result is a form of epistemic lock-in in which the scientific community’s collective knowledge trajectory reflects not only intrinsic material realities but also the path-dependent history of its algorithmic tools [3].
The core theoretical contribution of this analysis is the explicit linkage between algorithmic and scientific forms of path dependency in the specific context of materials AI.
In materials AI, algorithmic path dependency propagates into scientific path dependency such that early computational choices constrain not only model outputs but also the scientific questions that can be asked, the materials that can be discovered, and the knowledge that can be generated.
The linkage between algorithmic and scientific path dependency occurs through multiple reinforcing mechanisms (elaborated in subsequent sections). It produces long-term scientific consequences that are not easily reversed once established, rendering seemingly technical algorithmic decisions into de facto scientific commitments.
This theoretical linkage reframes materials AI not as a neutral tool but as an active participant in shaping scientific possibility spaces.
Table 1 distinguishes algorithmic from scientific path dependency across locus, mechanisms, switching costs, and forms of lock-in, clarifying why the former can systematically propagate into the latter in materials AI.
Table 1. Analytical Comparison of Algorithmic and Scientific Path Dependency in Materials AI
Dimension | Algorithmic path dependency | Scientific path dependency | Conceptual significance for the manuscript |
Primary locus | Model training pipeline and computational workflow | Research community, laboratory practice, and disciplinary agenda | Establishes that path dependency exists at both technical and epistemic levels |
Typical early choices | Initialization, data ordering, hyperparameters, descriptor selection, and stopping criteria | Problem framing, material selection, technique adoption, theoretical framing, and publication/funding priorities | Shows structural analogy between code-level and science-level commitments |
Immediate effect | Narrow reachable model states and output distributions | Narrows acceptable questions, candidate systems, and evidentiary standards | Clarifies that both forms reduce future option space |
Self-reinforcing mechanism | Optimization lock-in, representational inertia, reuse of trained pipelines, and benchmark dependence | Citation accumulation, grant reinforcement, experimental replication, and institutional legitimacy | Connects increasing returns logic to both domains |
Switching costs | Retraining, redesign, data reprocessing, loss of comparability, and engineering overhead | Reframing projects, rebuilding infrastructure, retraining personnel, reputational and funding risk | Explains why reversal is difficult even when better alternatives exist |
Observable manifestation | Persistent output sensitivity, constrained candidate ranking, and stable but narrow search behavior | Repeated focus on the same material classes, methods, and explanatory vocabularies | Makes path dependency empirically recognizable in practice |
Unit of lock-in | Model behavior and representational capacity | Research trajectory and knowledge canon | Distinguishes what becomes rigid at each level |
Temporal horizon | Emerges during development and deployment cycles | Accumulates across projects, publications, and field-level agenda-setting | Shows how short-run algorithmic choices become long-run scientific commitments |
Main risk | Invisible exclusion of alternative outputs | Invisible exclusion of alternative discoveries and theories | Sharpens the manuscript’s normative warning |
Preferred mitigation | Multi-seed training, alternative descriptors, reordered datasets, parallel models, and sensitivity analysis | Methodological pluralism, broader evaluation standards, and diversified funding/publication incentives | Links diagnosis to an actionable intervention |
Figure 1 conceptualizes how early algorithmic commitments propagate through four mediating mechanisms to generate downstream scientific path dependency in materials AI.
Figure 1. Algorithmic path dependency as scientific path dependency in materials AI
Algorithmic choices do not merely assist discovery; they co-determine the epistemic horizon of the field. For example, when a Bayesian active learning framework initialized with particular priors surfaces only certain compositional regions [14], the resulting experimental validations reinforce those same regions in future iterations, creating a feedback loop between code and laboratory practice. The seed paper on this exact conceptual linkage already anticipates that such propagation occurs systematically in materials AI pipelines [3].
Conceptually, the linkage can be visualized as a directed flow diagram in which algorithmic decision nodes at the top (initialization, data ordering, descriptor choice) feed downward through filtering, allocation, representation, and publication mechanisms into scientific constraint nodes at the bottom (question selection, material prioritization, knowledge canon formation). Arrows between nodes thicken over time to represent increasing returns and lock-in, while feedback loops ascend from scientific outcomes back to algorithmic refinement, illustrating the co-evolutionary nature of the dependency. This conceptual figure underscores that the propagation is bidirectional yet asymmetrically initiated by algorithmic choices in contemporary practice.
The claim rests on logical entailment rather than empirical induction: given the definitions established in prior sections and the documented sensitivity of both algorithmic and scientific processes to early conditions [1, 2, 8, 23], the propagation follows necessarily once materials AI is positioned as the primary interface between computation and discovery [4, 7]. Because algorithmic representations become the lens through which materials are perceived and prioritized, constraints at the algorithmic level become epistemic constraints at the scientific level [5, 13]. This perspective offers a novel theoretical bridge between computational science and the philosophy of science, highlighting how code-level path dependency acquires scientific significance in data-intensive discovery domains.
The theoretical linkage between algorithmic and scientific path dependency in materials AI is realized through four distinct yet interrelated mechanisms that translate early computational choices into enduring scientific constraints.
Table 2 specifies the concrete mechanisms by which early algorithmic choices are translated into downstream scientific constraints, identifying the intervention points at which lock-in can still be disrupted.
Table 2. Mechanisms through which algorithmic path dependency becomes scientific path dependency
Algorithmic trigger | Translating mechanism | Immediate downstream effect in the materials AI workflow | Long-run scientific consequence | Practical intervention point |
Random initialization privileges one optimization trajectory over others | Output filtering | Certain candidates are surfaced, ranked, or discarded early | Some material families remain underexplored despite potential promise | Require multi-seed comparison and report candidate-set variability |
Early training exposure favors particular chemistries or regimes | Output filtering + knowledge representation | Embedding space overrepresents initially encountered patterns | Subsequent hypotheses are biased toward already emphasized regions | Reorder training sequences and audit representation drift |
Initial hyperparameter settings stabilize one generalization pattern | Resource allocation | Experimental attention follows model outputs judged most promising | Time, synthesis effort, and grant resources accumulate around one path | Conduct robustness checks across hyperparameter regimes before experimental commitment |
Descriptor or feature-set choice encodes one view of structure–property relations | Knowledge representation | Some physical relations become legible while others remain invisible | The field’s explanatory vocabulary narrows around one representational scheme | Compare descriptor families and preserve plural representational baselines |
Early stopping criteria freeze a partially explored solution landscape | Output filtering | Candidate rankings reflect prematurely stabilized model behavior | Discovery trajectories become anchored to shallow local optima | Test late-training alternatives and flat-minima sensitivity |
Repeated publication of algorithmically favored successes | Publication bias | Positive results from one pipeline become a visible benchmark | Alternative approaches lose legitimacy, attention, and cumulative citations | Encourage null-result reporting and cross-path replication |
Experimental validation follows path-dependent rankings | Resource allocation + publication bias | Validated outputs feed back into future datasets and benchmark norms | Scientific canon formation reflects historical algorithmic contingencies | Diversify validation portfolios beyond top-ranked candidates |
Community reuse of dominant datasets and pipelines | All four mechanisms jointly | Standardized workflows become the default discovery infrastructure | Epistemic lock-in hardens across labs, journals, and funding systems | Build field-level reporting standards for path-sensitive design choices |
Output Filtering. Algorithmic path dependency determines which material candidates or property predictions are surfaced for human consideration [14]. Because models sensitive to initialization and data ordering prioritize certain compositional spaces over others [8, 12], many potentially valuable materials remain invisible to researchers. In active learning pipelines for materials discovery, early algorithmic decisions filter the candidate pool, meaning entire families of compounds are never experimentally pursued simply because they were not generated or ranked highly by the initial model configuration [7, 26].
Resource Allocation. Once filtered outputs are available, they directly influence the allocation of scarce experimental resources and funding. Algorithmic recommendations that emerge from path-dependent training processes guide which materials receive synthesis and characterization efforts [7]. This creates a self-reinforcing cycle whereby materials favored by the initial algorithmic path attract disproportionate attention and investment. In contrast, alternative materials are deprioritized, amplifying the scientific consequences of seemingly technical choices [10].
Knowledge Representation. The descriptors, embeddings, and internal representations learned by the algorithm shape what relationships can be discovered and articulated scientifically [5, 13]. When particular feature sets or graph representations are selected early and become locked in, they determine the conceptual vocabulary available for interpreting structure-property relationships. Alternative physical insights that might arise from different representations are structurally foreclosed, constraining the scientific theories that can be developed around AI-generated discoveries [18].
Publication Bias. Algorithmically driven successes are more likely to be published and cited, reinforcing the dominant path while alternative approaches or negative results remain undocumented [19]. This publication dynamic creates feedback loops where path-dependent algorithmic outcomes become the visible scientific record, making it increasingly difficult for researchers to justify exploration of different algorithmic or scientific directions [23].
Conceptually, these mechanisms can be represented as a flow from algorithmic decision nodes (initialization, data ordering, descriptor choice) through the four mechanisms downward to scientific constraint nodes (question selection, material prioritization, epistemic lock-in), with thickening arrows indicating increasing returns and upward feedback loops showing co-evolution between computation and laboratory practice.
From the central theoretical claim that algorithmic choices in materials AI create path dependency—where early decisions about initialization, data ordering, and descriptor sets irreversibly shape subsequent search trajectories—three important corollaries follow, each revealing structural constraints on scientific discovery. Corollary 1 asserts that the diversity of materials AI research outputs is inherently constrained by algorithmic path dependency, meaning that because early computational choices narrow both the explored parameter space and the forms of representable knowledge, the field collectively produces a less diverse set of discoveries than would be possible under genuine methodological pluralism, effectively biasing results toward known phases or easily accessible local minima [3, 26]. Corollary 2 argues that reversing scientific path dependency requires addressing algorithmic path dependency directly. Yet, this reversal is often practically impossible once lock-in has occurred, as high switching costs in legacy codebases, trained models, normalized descriptor sets, and established research programs create strong economic and social incentives to persist with suboptimal but entrenched methods [1, 2, 8]. Corollary 3 therefore prescribes that methodological pluralism—explicitly employing multiple random initializations, varied data orderings, diverse descriptor sets, and parallel algorithmic approaches such as different optimization strategies or surrogate models—is essential to mitigate path-dependent effects and genuinely expand the scientific possibility space in materials discovery, transforming what might otherwise be a deterministic narrowing into a more exploratory and robust search [11, 28, 29]. Taken together, these corollaries reveal that path dependency is not merely a technical nuisance to be optimized away. Still, rather a structural feature of AI-driven materials research with profound epistemic implications: it determines not only what gets discovered but what can be discovered, shaping the very boundaries of reproducible knowledge.
This theoretical analysis carries direct implications for research practice. Authors should explicitly report path-dependent choices such as initialization seeds and data ordering, conduct sensitivity analyses across alternative starting conditions, and acknowledge how their algorithmic decisions constrain the generalizability of findings [3, 15]. Reviewers ought to probe manuscripts for awareness of path dependency, question results based on single initializations, and request robustness checks against different algorithmic paths. At the community level, the field would benefit from developing standardized reporting guidelines for path-dependent decisions, incentivizing methodological pluralism through funding and publication norms, and retrospectively studying historical path dependencies in major materials AI breakthroughs [7, 14].
This paper has articulated that algorithmic path dependency propagates into scientific path dependency in materials AI. Early choices in model initialization, data sequencing, and descriptor selection are not merely technical but shape the very trajectory of scientific discovery. By drawing on foundational concepts from economics and applying them to contemporary materials in AI literature, the analysis reveals the need for greater awareness of how computational decisions become epistemic commitments. Recognizing and actively managing path dependency through pluralism and transparency will be critical if materials AI is to realize its full potential for open-ended discovery rather than constrained, self-reinforcing trajectories.
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