Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization

Cara Cronin; Corey A. Sanz; Jason E. Hein; Jason Hattrick-Simpers; Ryan P. Jansonius; Shayan S. Mousavi Masouleh

REVIEW 3 major objections 4 minor 10 references

Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper claims that a human-in-the-loop active learning framework can raise the magnesium tolerance of continuous lithium carbonate crystallization from roughly 80 ppm to several thousand ppm by running the cold reactor hotter than…

desk verdict The cold-reactor temperature effect is real and experimentally grounded; the '6000 ppm Mg tolerance' headline is GPC extrapolation with zero experimental support above 3000 ppm. read the letter →

arxiv 2507.19316 v1 pith:MLPR6MMF submitted 2025-07-25 cs.CE cond-mat.mtrl-scics.HCcs.LG

Shayan S. Mousavi Masouleh , Corey A. Sanz , Ryan P. Jansonius , Cara Cronin , Jason E. Hein , Jason Hattrick-Simpers This is my paper · ORCID

classification cs.CEcond-mat.mtrl-scics.HCcs.LG

keywords human-in-the-loopactivelearningcontinuouscrystallizationlithiumcarbonatemagnesiumimpuritytoleranceGaussianprocessclassifierbattery-gradelow-gradebrineoptimization

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper reports that a human-in-the-loop active learning framework, combining Gaussian process models with expert judgment, found a counterintuitive lever for producing battery-grade lithium carbonate: running the cold reactor of a continuous crystallizer well above the conventional 60 °C limit greatly improves rejection of magnesium impurities. The authors claim this raises tolerable initial magnesium contamination from the usual ~80 ppm battery-grade threshold to several thousand ppm, potentially 6000 ppm. If correct, lower-grade brines with high magnesium loads could be processed directly into battery-grade lithium carbonate with reduced pre-refinement. The paper also argues that human guidance, not automation alone, was what uncovered the effect: 38 experiments sufficed to establish the key temperature relationship, while simulated uninformed active learning on broad parameter ranges found battery-grade conditions far less often.

What carries the argument

The quantitative core of the work is a Gaussian process classifier (GPC), a probabilistic model that assigns each combination of initial impurity levels and reactor settings a probability of yielding battery-grade lithium carbonate. Its 0.5-probability contour is the decision boundary used to define how much initial magnesium can be tolerated. A ray-tracing algorithm selects experimental candidates nearest that boundary, and human experts adjust the surrogate space and feature ranges based on statistical diagnostics. The experimentally discovered inverse relation between cold reactor temperature and final magnesium concentration is the physical signal that the GPC encodes and extrapolates.

What would settle it

Run the continuous crystallization with initial Mg at 4000–6000 ppm, cold reactor at 70–80 °C, and otherwise matched conditions, then measure final Mg in the product; if final Mg does not stay below 80 ppm, or the product purity drops below battery grade, the extrapolated tolerance claim is false.

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Extended reading notes

Core claim

The paper's central claim is that continuous crystallization can produce battery-grade lithium carbonate from feedstocks carrying far more magnesium than the conventional ~80 ppm limit, provided the cold reactor is run hotter than previously recommended. The authors experimentally validated a counterintuitive inverse correlation: raising the cold reactor temperature (to roughly 68–80 °C in successful runs, versus the old ≤60 °C rule) lowers final Mg below the 80 ppm battery-grade threshold even when initial Mg is in the hundreds to several thousand ppm range. They formalize the boundary with a Gaussian process classifier whose decision frontier, at class probability 0.5, predicts battery-grade outcomes for initial Mg up to about 6000 ppm when cold reactor temperature is high enough. The claim is supported by 80 total experiments; 38 were needed to establish the temperature effect. No experiment with initial Mg above 3000 ppm appears in the tables as a battery-grade success, so the 6000 ppm tolerance rests on extrapolation of the fitted boundary rather than direct demonstration.

Load-bearing premise

The main load-bearing premise is that the model's boundary, learned from runs with up to 3000 ppm of initial magnesium, correctly predicts success near 6000 ppm even though no run at that level actually succeeded in the reported experiments.

Editorial extensions

If this is right

Feedstocks with hundreds to low-thousands of ppm Mg can be fed to the crystallizer and still yield battery-grade lithium carbonate when the cold reactor is hot enough.
The old rule of thumb (cold reactor ≤60 °C and at least 20 °C differential between reactors) is not a universal constraint; operating beyond it improves magnesium rejection.
Pre-refinement steps targeting magnesium can be reduced or skipped for many lower-grade brines, cutting water, reagent, and energy use.
Expert-guided active learning found the key parameter in 38 experiments, far fewer than the roughly 1,024 runs a full factorial design would require, and more reliably than uninformed computational search in the paper's simulated comparison.
The Gaussian process decision-boundary method gives an explicit operating map from initial Mg and cold-reactor temperature to battery-grade outcome, enabling process control decisions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper does not establish the physical mechanism behind the temperature effect; a solubility or dissolution-kinetics study at cold-reactor temperatures between 60 and 80 °C would test whether faster re-dissolution of impure solids is the cause.
A direct confirmatory run at 4000–6000 ppm initial Mg with a hot cold reactor would convert the extrapolated 6000 ppm tolerance from a model boundary into a demonstrated result.
The simulated comparison against uninformed Bayesian and random search uses a surrogate trained on the HITL data; a fully closed-loop comparison on fresh physical experiments would be a stronger test of the human-in-the-loop advantage.
The same decision-boundary active-learning recipe could generalize to other impurities and other continuous crystallizations, but that generalization is an extrapolation beyond the paper's evidence.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should read this one for the gap between its abstract and its own data, but there is a real result underneath.

The real result: in their hot-cold continuous crystallizer, raising the cold reactor temperature into the 63–70°C range — past the conventional ≤60°C limit — substantially improved magnesium rejection. The matched pairs with identical feed composition are the cleanest evidence: at ~1300 ppm initial Mg, cold reactor at 15°C gave 100–155 ppm final Mg, while 53–70°C gave 34–60 ppm. That counterintuitive, experimentally validated effect is new relative to the cited literature and could matter for low-grade brine processing. The HITL-AL workflow (GPR with Pareto fronts, random walkers, SHAP screens, GPC boundary exploitation) is competently assembled; the authors are transparent that model suggestions were filtered through human judgment; and the GitHub repo with data and scripts makes the experimental record checkable.

The soft spots, in proportion.

First, the abstract's headline — 'as high as 6000 ppm' Mg tolerance — is an extrapolation. The highest initial Mg that ever produced battery-grade product is Exp. 45: 3000 ppm initial, finishing at 73.9 ppm final, six ppm inside the 80 ppm cutoff and close to typical ICP-OES uncertainty. Every run above 3000 ppm failed, including the high-cold-temperature runs at 4028, 5000, and 6225 ppm initial Mg (final 122, 190, 464 ppm). The GPC boundary in Fig. 7 that would sustain a 6000 ppm claim crosses a region with zero positive observations, bracketed by failures; that boundary is kernel inductive bias, not evidence. The body's softer 'several thousand ppm' phrasing still leans on the same unverified extrapolation. The honest claim is 'up to roughly 3000 ppm,' or the authors run the bracketing experiments.

Second, the HITL-versus-autonomous comparison at the end is partly circular. The 'informed' surrogate space already encodes the human's temperature discovery, so the 67%-vs-14% success rates show the value of prior information, not of the human-in-the-loop mechanism itself. Fine as a simulation; the framing overclaims.

Third, data curation: Section 2.1 says 'significantly divergent' experiments were excluded post hoc, and Table S4 skips Exp. 31, 73, and 77 without comment.

Fourth, Table S4 carries no error bars anywhere; the single 3000 ppm success is a one-run anchor.

The process chemistry reads sound and the central mechanism claim holds up; the fixes are to the magnitude claim, the baseline framing, and the curation disclosure. This paper deserves a serious referee, with heavy revision expected. For hydrometallurgy and autonomous-discovery readers, the temperature finding is worth engaging with, and the claims-versus-evidence gap is itself a useful cautionary case.

Referee Report

3 major / 4 minor

Summary. This manuscript presents a human-in-the-loop active learning (HITL-AL) framework for optimizing continuous crystallization of lithium carbonate from high-impurity brines, with a focus on magnesium rejection. The central process claim is that raising the cold-reactor temperature beyond the conventional 60°C limit substantially improves Mg removal, increasing tolerable initial Mg concentrations from roughly 80 ppm (or a few hundred ppm) to as high as 6000 ppm. The experimental program comprises 80 runs, the first 38 of which were used to identify the temperature effect; the authors also compare HITL-AL against random and Bayesian active learning baselines using informed and uninformed simulated datasets. The paper includes statistical analyses (Pearson correlation, SHAP, sensitivity), a GPC-based decision boundary, and makes code and data available on GitHub.

Significance. If the temperature effect and the claimed tolerance levels hold, the work provides a practically valuable process insight for lithium purification from lower-grade brines and a useful demonstration of HITL-AL in a low-data chemical optimization setting. The matched-pair experiments (e.g., Exps. 38/40 vs. 39; 62 vs. 63/65) directly support the directional claim that higher cold-reactor temperature reduces final Mg concentration, and the provision of code and data on GitHub is a concrete reproducibility strength. However, the headline quantitative claim of 'as high as 6000 ppm' initial Mg tolerance is not supported by the experimental record and rests on extrapolation from a Gaussian process classifier trained on data with no successful run above 3000 ppm initial Mg.

major comments (3)

[Section 3 (Figure 7) and Abstract] The claim that the process tolerates initial Mg 'as high as 6000 ppm' is unsupported by the experimental data. In Table S4, the highest initial Mg that yields a battery-grade product (final Mg < 80 ppm) is Exp. 45 at 3000 ppm initial Mg, with a final Mg of 73.9 ppm, only 6 ppm below the threshold. Every run above 3000 ppm failed, including Exp. 78 (4028 ppm, T_cold 63°C -> 122 ppm), Exp. 48 (5000 ppm, T_cold 68°C -> 189.8 ppm), Exp. 68 (6225 ppm, T_cold 65°C -> 464 ppm), and Exp. 55 (7441 ppm, T_cold 79°C -> 933 ppm). The GPC boundary in Figure 7 therefore crosses the 3000–6000 ppm interval through a region with zero positive observations and negative bracketing points; with the Matern kernel length_scale of 0.3 listed in Table S6, the boundary location in that interval is set by the kernel's inductive bias rather than by evidence. The quantitative claim should be revised to reflect the demonstrated 3000 ppm limit, with any 'several thousand ppm' statement presented explicitly as an unverified model extrapolation, or the claim should be supported by new experiments in the 3000–6000 ppm range.
[Section 3 (Figure 9) and active learning comparison] The comparison against 'human-independent' active learning is not a fair test of the HITL interaction. The 'informed' dataset used for the Bayesian and random baselines is 'explicitly constrained by parameter ranges informed by the HITL-identified optimal temperature settings and impurity conditions.' This means the baselines are given the central process insight (elevated cold-reactor temperature) without any human-in-the-loop experimentation, so the reported 67% vs. 14% success rates demonstrate the value of that prior information rather than the value of the HITL workflow itself. The authors should either compare against baselines that receive the same experimental data stream without human intervention, or reframe the result as an ablation of informed priors rather than a demonstration of 'human-AI synergy.'
[Section 3 (Mg challenge) and Abstract] The baseline Mg tolerance is stated inconsistently, which changes the claimed improvement factor. The results section states that with initial Mg above approximately 80 ppm 'none of the preliminary experiments successfully reduced Mg below this desired limit,' and later that above 200 ppm success was 'consistently unattainable'; the abstract, however, describes the improvement as being from 'industry practices at a few hundred ppm.' These baselines differ by roughly an order of magnitude and should be harmonized, with explicit distinction between the conventional initial-Mg tolerance and the battery-grade final-Mg specification.

minor comments (4)

[Table S4] Experiment numbers 31, 73, and 77 are missing from Table S4; please renumber the entries or explicitly state that those runs were excluded, and if so, why.
[Figure 7] Figure 7 projects a GPC decision boundary onto the initial-Mg vs. T_cold plane, but the model was trained on additional features (Ca, K, Li, Na, flow rate, slurry concentration, temperature differential). Please clarify how the other features are fixed for this projection and whether the boundary location is sensitive to those choices.
[Section 2.1] The criteria for excluding 'failed' experiments after reproducibility tests are not quantified; providing the threshold or the number of excluded runs would improve transparency and reproducibility of the training dataset.
[Introduction] The phrase 'about more than approximately 1,024 experiments' is awkward and the factorial count depends on the number of levels per variable, which is not stated; please rephrase with a clear calculation.

Circularity Check

2 steps flagged · score 6.0 of 10

Headline 6000 ppm Mg-tolerance claim is a GPC extrapolation, and the HITL-vs-baseline benchmark is self-confirming via the 'informed' dataset; the experimentally validated cold-reactor effect is independent.

fitted input called prediction [Abstract; Section 3 (Fig. 7); Table S4 (Exps. 45, 78, 48, 68)]
"our results demonstrate the framework's ability to rapidly adapt to new data, improving the process's tolerance to critical impurities, such as magnesium, by industry practices at a few hundred ppm, and extending it to handle contamination levels as high as 6 000 ppm ... Figure 7 presents these GPR-derived predictions, distinguishing battery-grade from non-battery-grade outcomes, overlaid with all observed experimental data (totaling 80 experiments)."

The 6000 ppm tolerance is not an observed experimental result. The highest initial Mg that yielded battery-grade product is Exp. 45: 3000 ppm initial, T_cold=66°C, final Mg=73.9 ppm. Every bracketing high-T_cold run above 3000 ppm failed: Exp. 78 (4027.8 -> 122 ppm), Exp. 48 (5000 -> 189.8 ppm), Exp. 68 (6224.9 -> 464.3 ppm). The abstract's '6000 ppm' number is taken from the GPC decision-boundary projection in Fig. 7, i.e., from the surrogate fitted to these same experiments. In the unobserved 3000-6000 ppm interval, the boundary is set by kernel extrapolation and by the nearest negative training points, not by evidence. Presenting this model extrapolation as a demonstrated tolerance makes the headline quantitative claim an output of the fit rather than an independent result.
self definitional [Section 3, HITL-vs-baseline comparison (Fig. 9)]
"The first dataset, termed 'uninformed,' strictly adhered to initial parameter ranges without incorporating insights from the HITL process. The second dataset, termed 'informed,' was explicitly constrained by parameter ranges informed by the HITL-identified optimal temperature settings and impurity conditions. ... The simplified Bayesian approach leveraging the informed dataset ... identified battery-grade conditions within 40 experiments at a success rate of approximately 67%, significantly outperforming the uninformed dataset scenario, which achieved only 14% success."

The 'informed' dataset is constructed from the HITL-discovered optimal temperature and impurity ranges, so any sampling method operating on it sees a search space already enriched in battery-grade conditions. The 67% versus 14% outcome therefore restates the definition of the dataset (human knowledge was injected) and does not independently measure whether the HITL workflow caused the improvement. The asserted 'substantial advantage provided by incorporating human-derived knowledge' is true by construction of the informed dataset, making the benchmark self-confirming rather than an independent evaluation of the HITL loop.

full rationale

The experimentally validated directional mechanism—raising cold reactor temperature improves Mg rejection—is genuinely grounded: paired experiments (e.g., Exps. 38/40 vs 39, and the Fig. 5 validation) show higher T_cold reducing final Mg without relying on the fitted models. So the core physical insight is not circular. The partial circularity is concentrated in two places. First, the abstract's flagship quantitative claim of tolerance 'as high as 6000 ppm' is neither an observed outcome nor an independently verified prediction; it is read from the GPC decision-boundary projection, and the highest successful measured initial Mg is 3000 ppm with all higher bracketing runs failing. Second, the HITL-versus-baseline comparison defines the 'informed' dataset by the very HITL ranges it is supposed to validate, so the reported advantage is partly an artifact of dataset construction. There is no load-bearing self-citation chain: reference 25 is the authors' prior work but is cited only as one of several general active-learning references, and no uniqueness theorem or ansatz is smuggled in via citation. On the strict definition of circularity, the central quantitative headline reduces to a fitted surrogate's extrapolation, while the experimentally demonstrated temperature effect remains independent; hence score 6 rather than higher.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claims rest on fitted Gaussian process models, a laboratory crystallizer that stands in for industrial brines, and a post-hoc data exclusion rule. No new physical entities are introduced.

free parameters (3)

GPC kernel hyperparameters = ConstantKernel(1.0), Matern length_scale [0.3, 0.3], nu 1.5, WhiteKernel noise 0.06
Tuned via TPE to the experimental dataset; the decision boundary that supports the 6000 ppm tolerance depends on these fitted values.
GPR kernel hyperparameters = Matern length_scale 1, nu 1.5, alpha 1e-10
Used for Pareto frontier exploration during the exploratory phase; fitted to the first 36 runs.
Informed dataset bounds = Temperature and impurity ranges derived from HITL insights
The comparison study constrains the 'informed' simulations to HITL-identified ranges, which encodes the discovery and inflates the apparent advantage.

assumptions (3)

domain assumption Gaussian process surrogates trained on 38 to 80 experiments can generalize to untested regions of the parameter space.
The 6000 ppm tolerance claim and the decision boundary in Figure 7 rely on this extrapolation, which is not verified by direct runs above 3000 ppm.
domain assumption The two-reactor MSMPR continuous crystallizer with synthesized brines represents conditions relevant to real Smackover brines.
The authors use synthesized crude Li2CO3 to simulate real-world contamination; transferability to real brine is assumed throughout.
ad hoc to paper Excluding 'failed' experiments and adjusting quality scores based on reproducibility does not bias the model.
The exclusion rule is applied by experts after observing outcomes, which can selectively remove data that contradict the emerging hypothesis.

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Cite this review

Pith. "Pith review of Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization." pith.science (2026). https://pith.science/paper/MLPR6MMF

@misc{pith2026250719316,
  author       = {Pith},
  title        = {Pith review of: Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MLPR6MMF}},
  note         = {Machine review of arXiv:2507.19316}
}

read the original abstract

As demand for high-purity lithium surges with the growth of the electric vehicle (EV) industry, cost-effective extraction from lower-grade North American sources like the Smackover Formation is critical. These resources, unlike high-purity South American brines, require innovative purification techniques to be economically viable. Continuous crystallization is a promising method for producing battery-grade lithium carbonate, but its optimization is challenged by a complex parameter space and limited data. This study introduces a Human-in-the-Loop (HITL) assisted active learning framework to optimize the continuous crystallization of lithium carbonate. By integrating human expertise with data-driven insights, our approach accelerates the optimization of lithium extraction from challenging sources. Our results demonstrate the framework's ability to rapidly adapt to new data, significantly improving the process's tolerance to critical impurities like magnesium from the industry standard of a few hundred ppm to as high as 6000 ppm. This breakthrough makes the exploitation of low-grade, impurity-rich lithium resources feasible, potentially reducing the need for extensive pre-refinement processes. By leveraging artificial intelligence, we have refined operational parameters and demonstrated that lower-grade materials can be used without sacrificing product quality. This advancement is a significant step towards economically harnessing North America's vast lithium reserves, such as those in the Smackover Formation, and enhancing the sustainability of the global lithium supply chain.

Figures

Figures reproduced from arXiv: 2507.19316 by the authors.

**Figure 1.** Overview of the integration between the experimentation cycle and the human-in-the-loop active learning cycle, highlighting the different steps or modules within each phase. Upon initiating the HITL-AL cycle, as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗

**Figure 2.** (a) Simplified process flow diagram of the continuous hot-cold crystallization setup. (b) Image of benchtop setup used for processing LiCl brines into battery grade Li2CO3. The continuous crystallization process was designed to isolate battery-grade Li2CO3 from a Li2CO3 impure feedstock; see Figure 1a for a schematic overview. The initial crude material for this study was primarily synthesized to simulate various re… view at source ↗

**Figure 3.** GPR predictions and Pareto frontier extraction during the first four cycles of Bayesian active learning interactions on the 10,000 points in the surrogate space. As depicted in [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: a) Pearson correlation analysis illustrating the inverse relationship between cold reactor temperature and magnesium concentration across observed, LHC, and random walk-generated data. b) SHAP feature importance analysis revealing the impact of cold reactor temperature…

**Figure 5.** Figure 5: An experiment showcasing the impact of reactor temperatures on reducing impurity concentration, especially magnesium concentration (Table S5). Following the experimental validation of the inverse relationship between cold reactor temperature and Mg impurity concentrati…

**Figure 6.** Figure 6: a) Experiment refinement outcomes focused on the impact of initial Mg and temperature of cold reactor focused on initial Mg ranges below 12000 ppm. b) demonstration of ray tracing and decision boundary extraction process. c) Filtered experiments by selecting those clos…

**Figure 7.** Figure 7: Projection of GPC model predictions on the initial Mg concentration and cold reactor temperature plane, illustrating the decision boundary between battery-grade and non-battery-grade outcomes. This visualization emphasizes improved Mg contamination tolerance, demonstra…

**Figure 8.** Figure 8: provides additional analytical evidence supporting the significant influence of cold reactor temperature and initial Mg concentration on process outcomes. Specifically, [PITH_FULL_IMAGE:figures/full_fig_p025_8.png]

**Figure 9.** Figure 9: summarizes the comparative results of the simplified Bayesian and random active learning methods using both informed and uninformed datasets. The simplified Bayesian approach leveraging the informed dataset (integrating optimized parameter ranges derived from prior HIT…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

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Introduction Demand for high -purity lithium (Li), essential for batteries in electronics and vehicles, is projected to outstrip supply by 2035 1–3. As the demand continues to escalate, lithium-ion battery demand is expected to grow by 27% annually 4. This volatility underscores the urgency for more economically viable extraction methods as the traditiona...

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Data and Results Inspection Module

Methods This study introduces a Human-in-the-Loop Active Learning (HITL-AL) framework designed to optimize a continuous lithium crystallization process from low -grade brines, specifically brines with high levels of sodium (Na), magnesium (Mg), calcium (Ca), and po tassium (K). These impurities, prevalent in the Smackover Formation's brine sources, signif...

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This section delves deeper, providing more detailed information about each step. 2.2.1. Data and Results Inspection: In the Data and Results Inspection step, experimental (observed) and simulated (ML-predicted surrogate space) data were structured into comprehensive tables, including ICP-OES elemental concentrations results 13 (Ca, K, Li, Mg, Na in ppm) b...

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Results and Discussions Optimization of the continuous lithium crystallization process began with analysis of 16 preliminary experiments conducted before active learning integration. These initial experiments, selected based on expert judgment, consistently produced lithium carbonate (Li₂CO₃) with purity exceeding 99%, as shown in Table S2. Post-refinemen...

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

black box

Introduction Demand for high -purity lithium (Li), essential for batteries in electronics and vehicles, is projected to outstrip supply by 2035 1–3. As the demand continues to escalate, lithium-ion battery demand is expected to grow by 27% annually 4. This volatility underscores the urgency for more economically viable extraction methods as the traditiona...

work page

[2] [2]

Data and Results Inspection Module

Methods This study introduces a Human-in-the-Loop Active Learning (HITL-AL) framework designed to optimize a continuous lithium crystallization process from low -grade brines, specifically brines with high levels of sodium (Na), magnesium (Mg), calcium (Ca), and po tassium (K). These impurities, prevalent in the Smackover Formation's brine sources, signif...

work page

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This section delves deeper, providing more detailed information about each step. 2.2.1. Data and Results Inspection: In the Data and Results Inspection step, experimental (observed) and simulated (ML-predicted surrogate space) data were structured into comprehensive tables, including ICP-OES elemental concentrations results 13 (Ca, K, Li, Mg, Na in ppm) b...

work page

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uninformed,

Results and Discussions Optimization of the continuous lithium crystallization process began with analysis of 16 preliminary experiments conducted before active learning integration. These initial experiments, selected based on expert judgment, consistently produced lithium carbonate (Li₂CO₃) with purity exceeding 99%, as shown in Table S2. Post-refinemen...

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